Adding TeBlunthuis materials for UT Austin.

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/CV/Makefile

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+#!/usr/bin/make
+all: $(patsubst %.tex,%.pdf,$(wildcard *.tex)) 
+
+# use the following section for Rnw/knitr documents
+# all: $(patsubst %.Rnw,%.pdf,$(wildcard *.Rnw))
+# %.tex: %.Rnw
+#	Rscript -e "library(knitr); knit('$<')"
+
+%.pdf: %.tex 
+	latexmk -f -lualatex $<
+clean: 
+	latexmk -C *.tex
+	rm -f *.tmp *.run.xml
+	rm -f vc
+	rm -f *.bbl
+	# the following lines are useful for Rnw/knitr
+	# rm -rf cache/ figure/
+	# rm -f *.tex
+
+publish: all
+	rsync *.pdf groc:/var/www/nateprofesh/pdfs
+
+viewpdf: all
+	evince *.pdf
+
+vc: resources/vc-git
+
+pdf: all
+
+.PHONY: clean all
+

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/CV/resources/vc-git

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+#!/bin/sh
+# This is file 'vc' from the vc bundle for TeX.
+# The original file can be found at CTAN:support/vc.
+# This file is Public Domain.
+
+# Parse command line options.
+full=0
+mod=0
+while [ -n "$(echo $1 | grep '-')" ]; do
+    case $1 in 
+       -f ) full=1 ;;
+       -m ) mod=1 ;;
+       *  ) echo 'usage: vc [-f] [-m]'
+            exit 1
+    esac
+    shift
+done
+# English locale.
+LC_ALL=C
+git --no-pager log -1 HEAD --pretty=format:"Hash: %H%nAbr. Hash: %h%nParent Hashes: %P%nAbr. Parent Hashes: %p%nAuthor Name: %an%nAuthor Email: %ae%nAuthor Date: %ai%nCommitter Name: %cn%nCommitter Email: %ce%nCommitter Date: %ci%n" |gawk -v script=log -v full=$full -f ./vc-git.awk > vc
+if [ "$mod" = 1 ]
+then
+  git status |gawk -v script=status -f ~/bin/vc-git.awk >> vc
+fi

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/CV/resources/vc-git.awk

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+# This is file 'vc-git.awk' from the vc bundle for TeX.
+# The original file can be found at CTAN:support/vc.
+# This file is Public Domain.
+BEGIN {
+		
+### Process output of "git status".
+		if (script=="status") {
+				modified = 0
+		}
+
+}
+
+
+
+### Process output of "git log".
+script=="log" && /^Hash:/ { Hash = substr($0, 2+match($0, ":")) }
+script=="log" && /^Abr. Hash:/ { AbrHash = substr($0, 2+match($0, ":")) }
+script=="log" && /^Parent Hashes:/ { ParentHashes = substr($0, 2+match($0, ":")) }
+script=="log" && /^Abr. Parent Hashes:/ { AbrParentHashes = substr($0, 2+match($0, ":")) }
+script=="log" && /^Author Name:/ { AuthorName = substr($0, 2+match($0, ":")) }
+script=="log" && /^Author Email:/ { AuthorEmail = substr($0, 2+match($0, ":")) }
+script=="log" && /^Author Date:/ { AuthorDate = substr($0, 2+match($0, ":")) }
+script=="log" && /^Committer Name:/ { CommitterName = substr($0, 2+match($0, ":")) }
+script=="log" && /^Committer Email:/ { CommitterEmail = substr($0, 2+match($0, ":")) }
+script=="log" && /^Committer Date:/ { CommitterDate = substr($0, 2+match($0, ":")) }
+
+### Process output of "git status".
+### Changed index?
+script=="status" && /^# Changes to be committed:/ { modified = 1 }
+### Unstaged modifications?
+script=="status" && /^# Changed but not updated:/ { modified = 2 }
+
+
+
+END {
+		
+### Process output of "git log".
+		if (script=="log") {
+### Standard encoding is UTF-8.
+				if (Encoding == "") Encoding = "UTF-8"
+### Extract relevant information from variables.
+				LongDate = substr(AuthorDate, 1, 25)
+        DateRAW = substr(LongDate, 1, 10)
+        DateISO = DateRAW
+        DateTEX = DateISO
+        gsub("-", "/", DateTEX)
+				Time = substr(LongDate, 12, 14)
+### Write file identification to vc.tex.
+				print "%%% This file has been generated by the vc bundle for TeX."
+				print "%%% Do not edit this file!"
+				print "%%%"
+### Write Git specific macros.
+				print "%%% Define Git specific macros."
+				print "\\gdef\\GITHash{" Hash "}%"
+				print "\\gdef\\GITAbrHash{" AbrHash "}%"
+				print "\\gdef\\GITParentHashes{" ParentHashes "}%"
+				print "\\gdef\\GITAbrParentHashes{" AbrParentHashes "}%"
+				print "\\gdef\\GITAuthorName{" AuthorName "}%"
+				print "\\gdef\\GITAuthorEmail{" AuthorEmail "}%"
+				print "\\gdef\\GITAuthorDate{" AuthorDate "}%"
+				print "\\gdef\\GITCommitterName{" CommitterName "}%"
+				print "\\gdef\\GITCommitterEmail{" CommitterEmail "}%"
+				print "\\gdef\\GITCommitterDate{" CommitterDate "}%"
+### Write generic version control macros.
+				print "%%% Define generic version control macros."
+				print "\\gdef\\VCRevision{\\GITAbrHash}%"
+				print "\\gdef\\VCAuthor{\\GITAuthorName}%"
+        print "\\gdef\\VCDateRAW{" DateRAW "}%"
+        print "\\gdef\\VCDateISO{" DateISO "}%"
+        print "\\gdef\\VCDateTEX{" DateTEX "}%"
+				print "\\gdef\\VCTime{" Time "}%"
+				print "\\gdef\\VCModifiedText{\\textcolor{red}{with local modifications!}}%"
+				print "%%% Assume clean working copy."
+				print "\\gdef\\VCModified{0}%"
+				print "\\gdef\\VCRevisionMod{\\VCRevision}%"
+		}
+		
+### Process output of "git status".
+		if (script=="status") {
+				print "%%% Is working copy modified?"
+				print "\\gdef\\VCModified{" modified "}%"
+				if (modified==0) {
+						print "\\gdef\\VCRevisionMod{\\VCRevision}%"
+				} else {
+						print "\\gdef\\VCRevisionMod{\\VCRevision~\\VCModifiedText}%"
+				}
+		}
+		
+}

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/CV/teblunthuis_cv.tex

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+\documentclass[11pt]{article}
+
+% include data on fonts
+
+\usepackage[T1]{fontenc}
+\usepackage{textcomp}
+\renewcommand{\rmdefault}{ugm}
+\renewcommand{\sfdefault}{phv}
+\usepackage[garamond]{mathdesign}
+
+\usepackage[letterpaper,left=1.95in,right=1.3in,top=1.2in,bottom=1.2in]{geometry}
+
+% Metadata -- alter as neded
+\def\myemail{nathante@uw.edu}
+\def\myweb{https://teblunthuis.cc}
+
+% Git version tracking 
+%\input{vc}
+
+% Required style files
+\usepackage{url,fancyhdr}
+\PassOptionsToPackage{hyphens}{url}
+\renewcommand\UrlFont{\color{black}\rmfamily}
+
+% color for the links 
+\usepackage[usenames,dvipsnames]{color}
+
+% import and customize urls
+\usepackage[usenames,dvipsnames]{color}
+\usepackage[breaklinks]{hyperref}
+
+\hypersetup{colorlinks=true, linkcolor=Blue, citecolor=Black, filecolor=Blue,
+    urlcolor=Blue, unicode=true}
+
+% customize the titles so that they appear in the right margin
+\usepackage{titlesec}
+
+
+\setlength{\marginparwidth}{4.2in}
+\setlength{\titlewidth}{2.2in}
+
+\titleformat{\section}[leftmargin]
+{\normalfont 
+\fontsize{12}{15}
+\sffamily\bfseries\filleft}
+{}{0pt}{\color{BrickRed}}
+\titlespacing{\section}
+{1.0in}{1.5ex plus .1ex minus .2ex}{1pc}
+
+\titleformat{\subsection}
+{\normalfont \bf}
+{}{0pt}{}
+\titlespacing{\subsection}
+{0em}{-1.2em}{0em}
+
+% create a special cvlist environment to format the items
+\newenvironment{cvlist}{
+\begin{list}{}{\leftmargin=3.0em \itemindent=-3.0em}
+  \setlength{\itemsep}{0pt}
+  \setlength{\parskip}{0em}
+  \setlength{\parsep}{1em}
+  \setlength{\parindent}{0em}}
+{\vspace{1em}
+\end{list}}
+
+% set the default indent to nothing
+\setlength{\parindent}{0em}
+
+\usepackage{afterpage}
+% \usepackage{endnotes}
+% \renewcommand\theendnote{\fnsymbol{endnote}}
+
+\begin{document}
+% \baselineskip 12.5pt
+
+% Page layout
+\pagestyle{fancy}
+\renewcommand{\headrulewidth}{0pt}
+\fancyhead{}
+\fancyfoot{}
+
+% number on all /subsequent/ pages
+\afterpage{\rhead{{\scriptsize\thepage}}}
+
+
+% git revision control footer 
+%\rfoot{\texttt{\scriptsize \VCRevision\ on \VCDateTEX}}
+
+% Address and contact block
+\begin{minipage}[t]{0.68\textwidth}
+ \flushleft {\footnotesize University of Michigan\\
+   School of Information}
+  
+\end{minipage}
+\hfill     
+\begin{minipage}[t]{0.0in}
+% dummy (needed here)
+\end{minipage}
+\hfill
+\begin{minipage}[t]{0.3\textwidth}
+  \flushright \footnotesize Phone: (+1) 509.572.5081 \\ 
+  \href{mailto:\myemail}{\myemail} \\
+  \href{\myweb}{\myweb}
+\end{minipage}
+\hfill
+\vspace{2em}
+
+%% Name 
+\noindent{\Large {\textsc{\textbf{Nathan TeBlunthuis}}}}
+
+\medskip
+Research in online organizing, peer production, machine learning applications, and collective action to inform and evaluate solutions for online communities.
+\section{Academic Appointments}
+\subsection{University of Michigan}
+\begin{cvlist}
+\item 2023--. Postdoctoral Research Fellow, School of Information
+\end{cvlist}
+
+\subsection{Northwestern University}
+\begin{cvlist}
+\item 2021--2022. Postdoctoral Scholar, School of Communication
+\end{cvlist}
+
+\subsection{University of Washington}
+\begin{cvlist}
+\item 2015--2021. Research Assistant, Department of Communication
+\item 2019--2021. Instructor of Record, Department of Communication
+\item 2017--2019. Teaching Assistant, Department of Communication
+\end{cvlist}
+
+\section{Education}
+
+\subsection{University of Washington}
+\begin{cvlist}
+\item 2017--2021. PhD, Department of Communication. Committee: Benjamin Mako Hill (Chair), Aaron Shaw, David W. McDonald, Kirsten Foot. General examinations in (1) Computer Supportive Cooperatve Work (2) Organizational Communication (3) Communication Theory. Dissertation title: ``Ecology of Online Communities.''
+\item 2015--2017. MA, Department of Communication. Committee: Benjamin Mako Hill (Chair), W. Lance Bennett. Thesis title: ``Density Dependence without Resource Partitioning on an Online Petitioning Platform.''
+\end{cvlist}
+
+\subsection{Whitworth University}
+\begin{cvlist}
+\item 2008--2012. Bachelors of Science in \emph{Mathematics}.
+\item 2008--2012. Bachelors of Science in \emph{Computer Science}.
+\end{cvlist}
+
+\section{Industrial \& Nonprofit Experience}
+\begin{cvlist}
+\item 2019 \emph{Wikimedia Foundation}, Research Intern: Scoring Team. San Francisco, California, United States. Lead original research project investigating algorithmic bias and flagging systems in online platform governance. 
+\item 2018 \emph{Wikimedia Foundation}, Data Analyst Consultant: Growth Team. San Francisco, California, United States. Designed reading time metrics for Wikipedia and lead original research project published in OpenSym 2019. 
+\item 2012--2014. \emph{Microsoft Corporation}, Software Develement Engineer: Bing Multimedia UX. Bellevue, Washington. Built distributed back-end systems to drive multimedia engagement features on Bing.com. Trained search recommendation algorithms. 
+\item 2008--2010. \emph{Pacific Northwest National Laboratory}, Technical Intern: Nuclear waste vitrification. Richland, Washington. Developed method for measuring sample volume and expansion during melting in high-temperature furnace. 
+\end{cvlist}
+
+\section{Publications}
+\subsection{Articles in Peer Reviewed Journals and Conference Proceedings}
+\begin{cvlist}
+  \item TeBlunthuis, Nathan, Benjamin Mako Hill. 2022 ``Identifying Competitive and Mutualistic Relationships Between Online Communities.'' In \textit{International AAAI Conference on Web and Social Media (ICWSM 2022)} \url{https://ojs.aaai.org/index.php/ICWSM/article/view/19352/19124}.
+
+\item TeBlunthuis, Nathan, Charles Kiene, Isabella Brown, Nicole McGinnis, Laura (Alia) Levi, and Benjamin Mako Hill. 2022. ``No Community Can Do  Everything: Why People Participate in Similar Online Communities.'' In \textit{Proceedings of the ACM: Human-Computer Interaction (CSCW)}. \url{https://dl.acm.org/doi/10.1145/3512908}.
+
+\item TeBlunthuis, Nathan. 2021 ``Measuring Wikipedia Article Quality in One Dimension.'' In \textit{Proceedings of the 17th International Symposium on Open Collaboration (OpenSym '21)}. Online: ACM Press. \url{https://doi.org/10.1145/3479986.3479991}.
+
+\item TeBlunthuis, Nathan, Benjamin Mako Hill, and Aaron Halfaker. 2021 ``Effects of algorithmic flagging on fairness: Quasi-experimental evidence from Wikipedia.''  In \textit{Proceedings of the ACM: Human-Computer Interaction (CSCW)}.  56:1-56:27. \url{https://doi.org/10.1145/3449130}.
+
+\item Narayan, Sneha, Nathan TeBlunthuis, Wm Salt Hale, Benjamin Mako Hill, and Aaron Shaw. 2019. ``All Talk: How Increasing Interpersonal Communication on Wikis May Not Enhance Productivity.'' In \textit{Proceedings of the ACM: Human-Computer Interaction (CSCW)}: 101:1-101:19. \url{https://doi.org/10.1145/3359203}.
+
+\item TeBlunthuis, Nathan, Tilman Bayer, and Olga Vasileva. 2019. ``Dwelling on Wikipedia: Investigating Time Spent by Global encyclopedia Readers.'' In \textit{Proceedings of the 15th International Symposium on Open Collaboration (OpenSym '19)}. Skövde. Sweden: ACM Press. \url{https://doi.org/10.1145/3306446.3340829}.
+
+\item TeBlunthuis, Nathan, Aaron Shaw, and Benjamin Mako Hill. 2018. ``Revisiting `The Rise and Decline' in a Population of Peer Production Projects.'' In \textit{Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI '18)}. New York, New York: ACM Press. \url{https://doi.org/10.1145/3173574.3173929}.
+
+\item Weninger, Tim,  Marina Danilevsky, Fabio Fumarola, Joshua Hailpern, Jiawei Han, Thomas J Johntson, Surya Kallumadi, Hyungsul Kim, Zhijin Li, David McCloskey, Yizhou Sun, Nathan E TeGrotenhuis, Chi Wang, Xiao Yu. 2011. ``Winacs: Construction and analysis of web-based computer science information networks.'' In \emph{Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (SIGMOD ’11)}, 1255-1258. Athens, Greece: ACM Press. \url{https://doi.org/10.1145/1989323.1989469}.
+
+\item Hrma, Pavel, José Marcial, Kevin J Swearingen, Samuel H Henager, Michael J Schweiger, Nathan E TeGrotenhuis. 2010. ``Conversion of batch to molten glass, II: Dissolution of quartz particles.'' Journal of Non-Crystalline Solids 357 (3): 820-828. \url{https://doi.org/10.1016/j.jnoncrysol.2010.11.096}.
+
+\item Schweiger, Michael J, Pavel Hrma, Carissa J Humrickhouse, José Marcial, Brian J Riley, Nathan E TeGrotenhuis. 2010. ``Cluster Formation of Silica Particles in Glass Batches During Melting.'' Journal of Non-Crystalline Solids 356 (25): 1359-1367 \url{https://doi.org/10.1016/j.jnoncrysol.2010.04.009}.
+
+\item Hrma, Pavel, Michael J Schweiger, Carissa J Humrickhouse, J Adam Moody, Rachel M Tate, Timothy T Rainsdon, Nathan E Tegrotenhuis, Benjamin M Arrigoni, Jose Marcial, Carmen P Rodriguez, Benjamin H Tincher. 2010. ``Effect of Glass-Batch Makeup on the Melting Process.'' Ceramics-Silikaty 54 (3): 193-211. \url{http://www.ceramics-silikaty.cz/2010/pdf/2010_03_193.pdf}.
+
+\end{cvlist}
+\subsection{Other Scholarly Publications}
+\begin{cvlist}
+\item {[Book chapter]} TeBlunthuis, Nathan. 2022 ``Big Data'' In Andrea Ceron (Ed.), \textit{Encyclopedia of Technology \& Politics}. Northhampton, Massachusetts: Edward Elgar Publishing. (in press)
+
+\item {[Poster and Extended Abstract]} TeBlunthuis, Nathan, Aaron Shaw, and Benjamin Mako Hill. 2017. ``Density Dependence Without Resource Partitioning: Population Ecology on Change.org.''  \textit{Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17 Companion)}, 323–326. New York, New York: ACM Press. \url{https://doi.org/10.1145/3022198.3026358w}.
+
+% political turbulence book review
+\item {[Book review]} TeBlunthuis, Nathante. 2018. ``Helen Margetts, Peter John, Scott A Hale, Taha Yasseri, Political Turbulence: How Social Media Shape Collective Action.''  Journal of Information Technology \& Politics 15 (1): 1–2. \url{https://doi.org/10.1080/19331681.2017.1337603}
+\end{cvlist}
+
+\subsection{Working Papers}
+\begin{cvlist}
+
+% \item Kiene, Charles, Nathan TeBlunthuis, and Benjamin Mako Hill. ``Why These Rules?: Measuring How Adaptation and Leadership Styles Shapes Online Community Governance.'' (planned submission to \emph{CSCW})
+
+  \item TeBlunthius, Nathan.  ``Organizational Ecology of Online Communities'' (planned submission to \emph{Management Communication Quarterly})
+
+  \item TeBlunthius, Nathan. Valarie Hase, and Chung-hong Chan  ``Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!'' \url{https://arxiv.org/abs/2307.06483} (under review at \textit{Communication Methods and Measures} with a ``Minor Revisions'' decision.)
+
+  \item Coglazier, Carl. Nathan TeBlunthuis, and Aaron Shaw  ``The Effects of Group Sanctions on Participation and Toxicity: Quasi-experimental Evidence from the Fediverse.'' (planned submission to \emph{ICWSM})
+
+  \item TeBlunthius, Nathan.  ``Dynamics of Ecological Adaptation in Online Communities'' (planned submission to \emph{Nature Human Behavior})
+
+%%  \item Adekunle, Tiwaladeoluwa, Jeremy Foote, Nathan TeBlunthuis and Laura Nelson ``Co-Creating Risk Online: Exploring Conceptualizations of COVID-19 Risk in Ideologically Distinct Online Communities''
+
+  \item Foote, Jeremy, Nathan TeBlunthuis, Benjamin Mako Hill, and Aaron Shaw. ``How Individual Behaviors Drive Inequality in Online Community Sizes.'' (planned submission to \emph{CSCW})
+
+  \item TeBlunthius, Nathan, Aaron Shaw, and Benjamin Mako Hill.  ``Ecological Dynamics in Online Collective Action.'' (planned submission to \emph{Science})
+
+\end{cvlist}
+
+% IC2S2 poster
+\section{Presentations}
+\subsection{Refereed Paper Presentations (Non-Archival)}
+Presenting authors are marked a ``$^*$'' in the list below. Because conference or workshops norms about submission vary, some papers appear more than once.
+
+\begin{cvlist}
+\item TeBlunthuis, Nathan. ``Toward a Theory of Online Organizational Ecology''  National Communication Association Annual Convention, New Orleans, Nov 18th, 2022.
+  % \item  TeBlunthuis$^*$, Nathan, Charles Kiene, Isabella Brown, Nicole McGinnis, Laura (Alia) Levi, and Benjamin Mako Hill. 2022. ``No Community Can Do  Everything: Why People Participate in Similar Online Communities.'' In \textit{Proceedings of the ACM: Human-Computer Interaction (CSCW)}. \url{https://dl.acm.org/doi/10.1145/3512908}.
+  \item {[Poster]} TeBlunthius$^*$, Nathan., Valarie Hase, Chung-hong Chan  ``How to Stop Ignoring Classification Errors'' Text as Data Conference (TADA 2022), New York City, (Remote presentation) Oct 6th, 2022.
+  \item TeBlunthius$^*$, Nathan.  ``Dynamics of Ecological Adaptation in Online Communities'' International Conference on Computational Social Science (IC2S2 2022), Chicago, July 21st, 2022.
+  \item TeBlunthius$^*$, Nathan.  ``Identifying Competitive and Mutualistic Relationships Between Online Communities.'' International Conference on Computational Social Science (IC2S2 2022), Chicago, July 21st, 2022.
+  \item TeBlunthius$^*$, Nathan.  ``Dynamics of Ecological Adaptation in Online Communities'' Computational Methods Division, Annual Conference of the International Communication Association (ICA 2022), Paris, France, May 27th, 2022.
+  \item Adekunle, Tiwaladeoluwa$^*$., Jeremy Foote, Nathan TeBlunthuis and Laura Nelson ``Co-Creating Risk Online: Exploring Conceptualizations of COVID-19 Risk in Ideologically
+Distinct Online Communities'' Health Communication Division, Annual Conference of the International Communication Association (ICA 2022), Paris, France, May 29th, 2022.
+\item TeBlunthuis$^*$ Nathan. ``Measuring Wikipedia Article Quality in One Dimension'' Wiki Workshop held at The Web Conference 2022, Lyon, France. (Remote presentation), Apr 25th, 2022.
+\item TeBlunthuis$^*$ Nathan.  ``Ecology of Online Organizations'' Organizational Communication Mini-Conference (OCMC 2021), Lawrence, KS. (Remote presentation), Oct 23rd, 2021. 
+\item TeBlunthuis$^*$ Nathan., Benjamin Mako Hill. ``A Community Ecology Approach for Identifying Competitive and Mutualistic Relationships Between Online Communities'' Computational Methods Division, Annual Conference of the International Communication Association (ICA 2021), Cambridge, MA, (Remote presentation), May 27th, 2021.
+\item TeBlunthuis$^*$ Nathan., Aaron Halfaker, Benjamin Mako Hill. ``Algorithmic flags and Identity-Based Signals in Online Community Moderation'' Session on Social media 2, International Conference on Computational Social Science (IC2S2 2020), Cambridge, MA, (Remote presentation), July 19, 2020.
+\item TeBlunthuis$^*$ Nathan., Aaron Shaw, Benjamin Mako Hill. ``The Population Ecology of Online Collective Action.'' Session on Culture and fairness, International Conference on Computational Social Science (IC2S2 2020), Cambridge, MA, (Remote presentation), July 19, 2020.
+\item TeBlunthuis$^*$ Nathan., Aaron Shaw, Benjamin Mako Hill. ``The Population Ecology of Online Collective Action.'' Session on Collective Action, ACM Conference on Collective Intelligence (CI 2020), Boston, MA, (Remote presentation), June 18, 2020.
+\item Narayan, Sneha, Nathan TeBlunthuis$^*$, Wm Salt Hale, Benjamin Mako Hill, and Aaron Shaw. ``More Connected But Not More Productive: Analyzing Support for Interpersonal Communication in Wikis.'' Session on Computational Approaches to Health Communication. Computational Methods, Annual Conference of the International Communication Association (ICA 2019), Washington, DC, May 27, 2019.
+\item Foote, Jeremy D$^*$, Benjamin Mako Hill, and Nathan TeBlunthuis. ``An Agent-Based Model of Online Community Joining.''  Organizational Communication Mini-Conference (OCMC). New Brunswick, NJ, October 5, 2018.
+\item Foote, Jeremy D.$^*$, Benjamin Mako Hill, and Nathan TeBlunthuis. ``An Agent-Based Model of Online Community Joining.'' Session on Collective Behavior. International Conference on Computational Social Science (IC2S2 2018), Evanston, Illinois, July 14, 2018.
+\item {[Poster]} TeBlunthuis, Nathan. ``Discovering Clusters of Opinion in Wiki Surveys with Bayesian Latent Factor Analysis'' 4th Annual International Conference on Compuational Social Science (IC2S2 '18). Evanston, IL, July 13, 2018
+\item TeBlunthuis, Nathan$^*$, Aaron Shaw, and Benjamin Mako Hill. ``Revisiting `The Rise and Decline' in a Population of Peer Production Projects.'' Information Systems, Annual Conference of the International Communication Association (ICA 2018), Prague, Czechia, May 25, 2018.
+\item {[Poster]} TeBlunthuis, Nathan, Aaron Shaw, and Benjamin Mako Hill. 2017. ``Density Dependence Without Resource Partitioning: Population Ecology on Change. Org.'' In \textit{Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ’17 Companion)}, 323–326. New York, New York: ACM Press. \url{https://doi.org/10.1145/3022198.3026358}.
+\item TeBlunthuis, Nathan$^*$, Benjamin Mako Hill and Aaron Shaw. ``Density Dependence Without Resource Partitioning: A Population Ecology of Change.org.'' Session on Computational Methods for Studying Political Communication, Computational Methods, Annual Conference of the International Communication Association (ICA 2017), San Diego, California, May 29, 2017.
+\item TeBlunthuis, Nathan$^*$, Benjamin Mako Hill and Aaron Shaw. ``Resource Partitioning and Density Dependence on a Digital Mobilization Platform.'' Internet, Politics, and Policy Conference (IPP 2016), Oxford University, Oxford, UK, September 23, 2016.
+\item TeBlunthuis, Nathan$^*$, Benjamin Mako Hill and Aaron Shaw. ``Resource Partitioning and Density Dependence on a Digital Mobilization Platform.'' Section on Communication, Information Technology, and Media Sociology, American Sociological Association Annual Meeting (ASA 2016), Seattle, Washington, August 23, 2016.
+% rise and decline dataset
+\end{cvlist}
+
+\subsection{Invited Presentations}
+\begin{cvlist}
+\item TeBlunthuis, Nathan, 2023 ``Ecology of Online Communities.''  Data Science Seminar, University of Michigan School of Information, February 2, 2023.
+
+\item TeBlunthuis, Nathan, Benjamin Mako Hill, and Aaron Halfaker. 2021 ``Effects of algorithmic flagging on fairness: Quasi-experimental evidence from Wikipedia.''  Wikimedia Research Showcase, Online, November 17, 2021.
+\item TeBlunthuis, Nathan$^*$, Aaron Shaw, and Benjamin Mako Hill. ``Revisiting `The Rise and Decline' in a Population of Peer Production Projects.'' Wikimedia Research Showcase, Online, April 18, 2018.
+\end{cvlist}
+
+\subsection{Datasets}
+\begin{cvlist}
+\item TeBlunthuis, Nathan, 2021, ``Replication Data for: Measuring Wikipedia Article Quality in One Dimension by Extending ORES with Ordinal Regression'', \url{https://doi.org/10.7910/DVN/U5V0G1}, Harvard Dataverse
+
+\item  TeBlunthuis, Nathan; Hill, Benjamin Mako; Halfaker, Aaron, 2021, ``Replication data for: Effects of algorithmic flagging on fairness: Quasi-experimental evidence from Wikipedia'', \url{https://doi.org/10.7910/DVN/E0RYJ4}, Harvard Dataverse
+
+\item TeBlunthuis, Nathan; Benjamin Mako Hill; Aaron Shaw, 2018, ``Replication Data for Revisiting `The Rise and Decline' in a Population of Peer Production Projects'', \url{https://doi.org/10.7910/DVN/SG3LP1}, Harvard Dataverse
+\end{cvlist}
+
+\subsection{Theses}
+
+\begin{cvlist}
+\item TeBlunthuis, Nathan. ``Ecology of Online Communities'' 2021. University of Washington.
+\item TeBlunthuis, Nathan. ``Density Dependence without Resource Partitioning on an Online Petitioning Platform'' 2017. University of Washington. \url{https://digital.lib.washington.edu:443/researchworks/handle/1773/41690} 
+\end{cvlist}
+
+% IOR on online communities
+% TA on org com
+% TA /  ecology and innovation
+\section{Teaching}
+\subsection{Courses}
+\begin{cvlist}
+\item 2021, Winter. Interpersonal Media: Online Communities (COM 482). UW Department of Communication's undergraduate program. (Instructor of Record)
+\item 2019, Spring. Interpersonal Media: Online Communities (COM 482). UW Department of Communication's undergraduate program. (Instructor of Record)
+\item 2019, Winter. Organizational Communication (COM 377). UW Department of Communication's undergraduate program. (TA)
+\item 2017, Fall. Innovation Communities (COM597B -- Masters Level). UW Communication Leadership’s “Masters in Communication in Communities and Networks” program. (TA)
+\end{cvlist}
+
+\subsection{Directed Research Groups}
+I have lead or co-lead the following ``Directed Research Groups'' (DRGs) that students enroll in as independent studies for credit but which are organized more like small project-based classes. Typically a cohort of enrolled students meets weekly with myself and a faculty advisor as part of a group research project.
+
+\begin{cvlist}
+\item 2021, Winter, Spring. Directed Research Group on qualitative analysis of online community ecology. (2 students; organized with Benjamin Mako Hill) (COM499--Undergraduate). % 2 students, 2cr alia, nicole (bella from REU)
+\item 2022, Spring. Directed Research Group on the construction of rules and policy in Wikia wikis. (2 students; organized with Aaron Shaw) (COM499--Undergraduate). % 2 students, Davida, Paz
+\end{cvlist}
+
+
+% ta lecture goes here
+
+\subsection{Guest Lectures}
+\begin{cvlist}
+\item  TeBlunthuis$^*$ Nathan., Aaron Halfaker, Benjamin Mako Hill ``Effects of Algorithmic Flagging on Fairness: Quasi-Experimental Evidence from Wikipedia.'' Research Methods (CS 499), Whitworth University. Spokane, WA, November 16, 2021. 
+\item TeBlunthuis, Nathan ``Ecological Perspectives on Innovation Communities.'' UW Communication Leadership’s “Masters in Communication in Communities and Networks” program. Seattle, WA, May 11, 2021. 
+\item  TeBlunthuis$^*$ Nathan., Aaron Halfaker, Benjamin Mako Hill ``Algorithmic flags and Identity-Based Signals in Online Community Moderation.'' Introduction to Data Science (CS 251), Whitworth University. Spokane, WA, May 3, 2021. 
+\item   TeBlunthuis$^*$ Nathan., Aaron Halfaker, Benjamin Mako Hill ``Algorithmic flags and Identity-Based Signals in Online Community Moderation.'' Algorithms and Society (SI 431). Ann Harbor, MI, February 18, 2021. 
+\item TeBlunthuis, Nathan ``Ecology of Online Communities.'' UW Communication (COM 482). Seattle, WA, March 04, 2020. 
+\item TeBlunthuis, Nathan ``Ecological Perspectives on Innovation Communities.'' UW Communication Leadership’s “Masters in Communication in Communities and Networks” program. Seattle, WA, November 16, 2017. 
+\end{cvlist}
+
+\section{Grants \& Awards}
+
+\subsection{Grants}
+% GRFP goes here
+\begin{cvlist}
+\item 2019-08-15--2022-07-31. \textit{National Science Foundation} (IIS-1908850) for ``Modeling the Ecological Dynamics of Online Organizations.'' Named personnel. Coauthored grant to support dissertation. (Total: \$497,724; UW: \$279,966)  
+\item 2015--2020. \textit{National Science Foundation} “Graduate Research Fellowship Program”  (\$108,000)
+\end{cvlist}
+
+\subsection{Awards}
+\begin{cvlist}
+\item 2023. \textit{Top Paper Award} from the \textit{Computational Methods Division} at the \textit{73rd annual International Communication Association Conference} for ``Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!''
+\item 2022. \textit{Top Paper Award} from the \textit{Computational Methods Division} at the \textit{72st annual International Communication Association Conference} for ``Dynamics of Ecological Adaptation in Online Communities.''
+\item 2022. \textit{Faculty Award for Outstanding Research—Ph.D. Dissertation} from the \textit{Department of Communication} at the \textit{University of Washington} for ``Ecology of Online Communities.''
+\item 2021. \textit{Top Paper Award} from the \textit{Computational Methods Division} at the \textit{71st annual International Communication Association Conference} for ``A Community Ecology Approach for Identifying Competitive and Mutualistic Relationships Between Online Communities.''
+\end{cvlist}
+
+\section{Service}
+
+\subsection{Research Affiliations}
+\begin{cvlist}
+\item 2015--. Member, \emph{Community Data Science Collective}.
+\item 2015--. Member, Design Use Build (dub), Human Computer Interaction Group, Uni-
+versity of Washington.
+\end{cvlist}
+
+\subsection{Professional Organization Membership}
+
+\begin{cvlist}
+\item 2016--. Member, \textit{International Communication Association}. Section Membership: Computational Methods, Organizational Communication.
+\item 2017--. Member. \textit{Association for Computing Machinery}. Section Membership: SIGCHI, CSCW.
+\end{cvlist}
+
+\subsection{Reviewing}
+
+I am a reviewer for the following journals, conferences, grant organizations, and academic presses in communication and human computer interaction:
+
+\bigskip
+ACM Conference on Computer-supported Cooperative Work and Social Computing (CSCW),
+ACM Conference on Human Factors in Computing (CHI),
+Future Internet,
+International Journal of Communication (IJOC),
+International Communication Association Annual Meeting (ICA),
+National Communication Association Annual Convention (NCA),
+Journal of Computational Social Science (JCSS),
+Studies in Communication and Media (SCM),
+The Internaional Symposium on Open Collaboration (OpenSym),
+PNAS NEXUS,
+Journal of Internet Services and Applications (JISA),
+Wikimedia Research Fund,
+Swedish National Science Foundation
+\medskip
+
+ I have received ``Special Recognitions for Outstanding Reviews'' from ACM's SIGCHI for reviews submitted for CSCW 2019,  CSCW 2020, and CSCW 2021.
+
+% these need to be here because of the missing cvlist
+\bigskip
+\bigskip
+
+% Wikimedia talk goes here
+% Department lightning talk goes here
+% library lightning talk goes here
+\subsection{Public Talks}
+
+\begin{cvlist}
+\item TeBlunthuis, Nathan, Valerie Hase, and Chung-Hong Chan. ``Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can!'' Cascadia R Conference. Seattle, Washington, August 19 2023.
+\item TeBlunthuis, Nathan, Tilman Bayer, and Olga Vasileva. ``Dwelling on Wikipedia: Investigating Time Spent by Global encyclopedia Readers.'' Wikimania. Stockholm, Sweden, August 17 2019.
+\item TeBlunthuis, Nathan, ``Personal Story Telling and Grassroots Recruitment'', Scholar’s Studio, University of Washington, Seattle, Washington, February 16 2017
+\end{cvlist}
+
+\subsection{Workshops \& Seminars}
+\begin{cvlist}
+\item Spring \& Fall; 2015, Spring \& Fall. 2016, Spring \& Winter 2020. Mentor, Community Data Science Workshops, Department of Communication and eScience Institute, University of Washington.
+
+\item Fall. 2016, Spring \& Winter 2020. Lecturer, Community Data Science Workshops, Department of Communication and eScience Institute, University of Washington.
+
+\end{cvlist}
+
+\subsection{Other Service Activities}
+
+\begin{cvlist}
+\item 2021--2023. \emph{WikeEducation Foundation} Faculty Mentor.
+\end{cvlist}
+
+\section{Journalism and Legal Research}
+\begin{cvlist}
+
+\item Contributed analysis as a source to ``Are ‘bots’ manipulating the 2020 conversation? Here’s what’s changed since 2016.'' Washington Post. November 20, 2019. 
+
+\item Contributed analysis and visualization to forthcoming article in Bloomberg Green with journalist Alex Lubben.
+
+\item Privately contributed statistical analysis to admitted evidence in \textit{Lafferty et al vs. Jones} showing that Infowars' online community highly engaged with conspiracy content. 
+
+\end{cvlist}
+
+% \section{Technical Skills}
+
+% \subsection{Programming Languages}
+% \begin{cvlist}
+% \item 8+ years experience: Python, R, Bash
+% \item 3+ years experience: C\#, F\#, C++
+% \item Some experience: Ruby, Java, Scala, Rust, Javascript
+% \end{cvlist}
+
+% \subsection{Data Engineering}
+% \begin{cvlist}
+% \item 6+ years experience: Apache Spark, Apache Arrow, SQL databases, High performance computing (Slurm, Torque)
+% \end{cvlist}
+
+% \subsection{Machine learning frameworks / Probabilistic Programming Languages}
+% \begin{cvlist}
+% \item 3+ years experience: Stan-MC
+% \end{cvlist}
+
+% \subsection{Typesetting}
+% \begin{cvlist}
+% \item 8+ years experience: \LaTeX
+% \end{cvlist}
+
+% \subsection{Operating Systems}
+% \begin{cvlist}
+% \item 8+ years experience: Linux (primarily Debian)
+% \item 3+ years experience: Windows
+% \end{cvlist}
+
+\end{document}
+
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+% LocalWords:  Marwick Poell Repurposing Jullien Sorin Matei Goggins
+% LocalWords:  Springer SIGCSE Dataverse ICA Gan DML IPP Shorey CSSS
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job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/CV/vc.aux

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+\relax 

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/CV/vc.fdb_latexmk

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+# Fdb version 3
+["pdflatex"] 1576376187 "vc" "vc.pdf" "vc" 1576376191
+  (generated)
+  "vc.pdf"
+  "vc.log"

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/letter/Makefile

@@ -0,0 +1,27 @@
+#!/usr/bin/make
+
+ENC_SIGIMAGE = figures/signature.pdf.gpg
+DEC_SIGIMAGE = figures/signature.pdf
+
+# to use encrypted signatures files, add "figures/signature.pdf" (no
+# quotes) right before the first $( in the following line:
+all: $(patsubst %.tex,%.pdf,$(wildcard *.tex))
+
+figures/signature.pdf:
+	gpg --yes --output $(DEC_SIGIMAGE) --decrypt $(ENC_SIGIMAGE)
+
+%.pdf: %.tex
+	latexmk -f -xelatex $<
+
+clean:
+	latexmk -C *.tex
+	$(RM) -f *.tmp *.run.xml
+	# to use encrypted signature files, uncomment the following line
+	# $(RM) -f $(DEC_SIGIMAGE)
+
+viewpdf: all
+	evince *.pdf
+
+pdf: all
+
+.PHONY: clean all update-sig

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/letter/auto/letter.el

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+(TeX-add-style-hook
+ "letter"
+ (lambda ()
+   (TeX-add-to-alist 'LaTeX-provided-class-options
+                     '(("letter" "11pt")))
+   (TeX-add-to-alist 'LaTeX-provided-package-options
+                     '(("geometry" "letterpaper" "left=1.2in" "right=1.2in" "top=1.2in" "bottom=1.2in") ("hyperref" "colorlinks=false" "pdfborder={0 0 0}" "") ("inputenc" "utf8x") ("fontenc" "T1") ("mathdesign" "garamond") ("color" "usenames" "dvipsnames") ("biblatex" "natbib=true" "style=numeric" "backend=biber")))
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "path")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "url")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "nolinkurl")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "hyperbaseurl")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "hyperimage")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "href")
+   (add-to-list 'LaTeX-verbatim-macros-with-delims-local "path")
+   (TeX-run-style-hooks
+    "latex2e"
+    "letter/structure"
+    "letter11"
+    "geometry"
+    "hyperref"
+    "inputenc"
+    "fontenc"
+    "textcomp"
+    "mathdesign"
+    "color"
+    "biblatex"
+    "graphicx"
+    "url"
+    "tikz"
+    "fontspec")
+   (TeX-add-symbols
+    "addressee"
+    "position"
+    "posshort"
+    "recipientdetailsblock")
+   (LaTeX-add-bibliographies
+    "refs"))
+ :latex)
+

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/letter/letter.tex

@@ -0,0 +1,338 @@
+\documentclass[11pt]{letter}
+\usepackage[letterpaper,left=1.2in,right=1.2in,top=1.2in,bottom=1.2in]{geometry}
+\usepackage[colorlinks=false,
+            pdfborder={0 0 0},
+            ]{hyperref}
+%\usepackage{ucs}
+\usepackage[utf8x]{inputenc}
+
+\usepackage[T1]{fontenc}
+\usepackage{textcomp}
+\renewcommand{\rmdefault}{ugm}
+\renewcommand{\sfdefault}{phv}
+\usepackage[garamond]{mathdesign}
+\usepackage[usenames,dvipsnames]{color}
+\usepackage[natbib=true, style=numeric, backend=biber]{biblatex}
+\addbibresource{refs.bib}
+\DeclareLanguageMapping{american}{american-apa}
+
+% For managing logo and signature
+\usepackage{graphicx}
+\usepackage{url}
+\usepackage{tikz}
+\usetikzlibrary{shapes,shapes.misc,backgrounds,fit,positioning}
+\tikzstyle{every picture}+=[overlay,remember picture]
+\usepackage{fontspec}
+
+
+\makeatletter
+
+\input{letter/structure.tex}
+
+
+\authordetails{
+School of Information\\
+University of Michigan\\
+105 S State St.\\
+Ann Arbor, MI 48109-1285\\
+}
+
+\logo{umich-si-logo-horizontal.jpg}
+\Who{Nathan TeBlunthuis}
+\Title{Postdoctoral Research Fellow}
+\email{nathante@umich.edu}
+
+
+% I use these when I'm generating many letters from stock material. They sometimes help me avoid sad mistakes.
+\newcommand{\addressee}{}
+\newcommand{\position}{}
+\newcommand{\posshort}{}
+\usepackage[natbib=true, style=numeric, backend=biber]{biblatex}
+\addbibresource{refs.bib}
+\DeclareLanguageMapping{american}{american-apa}
+
+
+\newcommand{\recipientdetailsblock}{
+
+\parbox[t]{0.55\textwidth}{
+\footnotesize
+\raggedright
+%% [[CHANGEME BELOW!]]
+\textcolor{Black}{
+  Social Informatics Faculty Search \\
+  School of Information \\
+  University of Texas at Austin \\
+  1616 Guadalupe St, Suite \#5.202 \\
+  Austin, Texas 78701-1213 \\
+  \today } } % Print the to name and address
+
+}
+\begin{document}
+\setmainfont{Arial}
+\begin{letter}
+
+
+% TODO get a copy of Northwestern Communication vertical lockup
+\tikz[overlay, shift=(current page.north west)]{\node [xshift=2.95in,
+  yshift=-1.35in]{\includegraphics[width=3.5in]{\logo}};}
+
+\tikz[overlay, shift=(current page.north east)]{\node [xshift=-2.7in,
+  yshift=-1.45in]{\authordetailsblock};}
+
+\tikz[overlay, shift=(current page.north west)]{\node [xshift=2.87in,
+  yshift=-2.4in]{\recipientdetailsblock};}
+
+
+
+% In case you want to use a scanned copy of your signature
+\signature{\vspace{-3.5em}\includegraphics[width=0.35\textwidth]{sig.png}\\
+\Who\\
+\vspace{1em}
+\small{
+\emph{\email}\\
+\emph{\Title}\\
+School of Information\\
+University of Michigan
+}
+}
+
+\vspace{3em}
+
+% CHANGEME
+\opening{Dear members of the search committee,
+}
+
+I am writing to apply for the position of Assistant Professor in the
+Department of Information Systems at the University of Maryland,
+Baltimore County. I am a postdoctoral research fellow in the School of
+Information at the University of Michigan advised by Ceren Budak. I
+completed my Ph.D. in the fall of 2021 in the Department of
+Communication at the University of Washington (UW) and was advised by
+Benjamin Mako Hill. My scholarship is about human-centered computing
+and artificial intelligence.
+
+ % Contact
+% information for my references Professors Ceren Budak, Benjamin Mako
+% Hill, and Aaron Shaw follows my signature.
+
+The core question of my scholarship is: ``When people organize around
+a common interest using digital media, how do the media technologies
+they use shape (and are shaped by) the practices and outcomes of their
+efforts?'' This is important to me because people use digital media to
+self-organize incredible collaborative and collective efforts, but
+doing so often depends on the use of rapidly changing technology they
+do not control and that can harm or under-support their efforts.
+Therefore, much of my research focuses on digital environments such as
+Wikipedia and Reddit that have afforded self-organizers considerable
+flexibility to shape their technology and institute policies, As a
+basis of comparison, some of my work looks at platforms, such as
+Change.org, that do not. I use a broad toolkit of data science methods
+in this work and I specialize in analyzing behavioral digital trace
+data using big data systems, machine learning algorithms, causal
+inference from observational data, and statistical methodology. I am
+an innovator and emerging leader in such methods within the field of
+communication --- the computational methods division of the
+International Communication Association has honored my first-authored
+work with top paper awards in three consecutive years.
+
+% . Many of my projects, look by . The second type, Others look into
+% platforms 
+% My research studies design changes in
+% into how people self-organize in digital environments like Wikipedia
+% where power to shape the technology it uses to better support its
+% work. considerable technical and
+
+
+% I study problems related to technology and organization
+% in a range of digital environments. Like Wikipedia and Reddit, some of
+% these afford communities considerable technical and organizational
+% flexibility. 
+
+% Questions of responsible data science are increasingly core to my
+% work, as exemplified by my two writing samples. The first, titled
+% ``\textit{Effects of Algorithmic Flagging on Fairness}'', is published
+% in \textit{Proceedings of the ACM: Human-Computer Interaction: CSCW},
+% and I discuss it in depth below. The second, under review with a
+% ``minor revisions'' decision at the journal \textit{Communication
+%   Methods and Measures}, addresses an important problem in modern
+% study designs in a broad range of social science which incorporate
+% supervised machine learning tools for measuring unstructured data
+% (e.g., to categorize social media posts as ``toxic''). The problem is
+% that such tools are prone to measurement error that may be correlated
+% with social categories. When input into statistical procedures,
+% classification errors can cause misleading inferences. My article uses
+% monte-carlo simulations to evaluate recently proposed methods for
+% adjusting regression models to account for such forms of measurement
+% error and introduces a new method that can use validation data to
+% obtain a consistent estimator that is more precise than can be
+% obtained using validation data alone. My future research plans,
+% discussed below, are to continue developing this line of
+% methodological research and to open an investigation into how
+% generative models may restructure the ecosystem of online voluntary
+% organizations.
+
+As suited to my broad and multi-faceted research questions, I take a
+pragmatic and pluralistic approach to scholarship that engages many
+areas of social and behavioral science including organizational
+sociology, communication, human-computer interaction, and computer
+supported cooperative work. My first-authored research is published in
+competitive and high-impact interdisciplinary outlets including the
+\textit{ACM CHI Conference on Human Factors in Computing Systems
+  (CHI)} (25\% acceptance rate), \textit{International AAAI Conference
+  on Web and Social Media (ICWSM)} (17\% acceptance rate), and
+\textit{Proceedings of the ACM: Human-Computer Interaction: Computer
+  Supported Cooperative Work (ACM CSCW)} (24\% acceptance
+rate)\footnote{ACM CSCW changed from an archival conference into a
+  journal in 2020 and stopped publishing acceptance rates.}. I also
+have a paper on how algorithmic bias contaminates social science
+research and how this can be addressed under review with a ``minor
+revisions'' decision at the journal \textit{Communication Methods and
+  Measures}. My ongoing projects continue targeting such outlets as
+well as top general-science journals that publish social and
+behavioral research.
+
+My 2021 \textit{ACM CSCW} article, ``Effects of Algorithmic Flagging
+on Fairness: Quasi-Experimental Evidence from Wikipedia'' exemplifies
+how my scholarship brings together online communities and artificial
+intelligence. I investigated how machine learning algorithms used by
+platform moderators to flag harmful behavior shape the fairness of
+their moderation actions. Algorithmic fairness research has uncovered
+how models can learn spurious associations between social signifiers
+(e.g., someone's skin color or use of a Wikipedia account) and
+predicted outcomes (e.g., whether someone attends their court date or
+vandalizes Wikipedia) in ways that risk amplifying social inequalities
+when models are used to make decisions. Yet scholars in science and
+technology studies have pointed out that focusing narrowly on
+model-building as the location of problems and their solutions ignores
+how social inequalities are sustained by broader social forces.
+
+To understand how algorithmic flags shape the fairness of moderation
+on Wikipedia, I used a regression discontinuity analysis to estimate
+causal effects of flagging algorithms on moderation actions in several
+Wikipedia language communities. Leading research groups at Carnegie
+Mellon, École Polytechnique Fédérale de Lausanne (EPFL), and Meta have
+already used the method that I developed in their AI Fairness,
+Accountability, and Transparency research. I found that even though
+the algorithm was biased against editors lacking important social
+signals of trustworthiness on Wikipedia, algorithmic flagging made
+moderation more fair to these editors because it helped moderators
+find and correct misbehavior by apparently trustworthy users. This
+demonstrates that AI technologies can be designed to promote equity in
+ways that go beyond eliminating bias.
+
+
+% My scholarship and interests connect to a through line that I see
+% across recent work by many members of your division: Our contemporary
+% media systems, social media 
+
+% Think about beyond the college / university i could work with 
+% Namedrop the other stuff
+I am particularly enthusiastic about your department because of its
+impressive faculty in human-centered computing and data science and am
+eager to collaborate where our interests intersect.
+
+Tera Reynolds
+Ozok ?
+Mentis ?
+Komlodi ?
+Pan ? 
+
+% Most notably, Professors , Fleischmann, Lease, Lee, Li,
+% and Slota all research issues related to ethical design and use of
+% algorithmic systems in ways connecting to my work on how such systems shape
+% ethical behavior in organizational contexts. In addition, my interests
+% in voluntary organizations that produce information goods such as
+% encyclopedias and software are deeply connected to James Howison's
+% scholarship on open source and scientific software production. 
+
+I would bring a distinctive organization science perspective to your
+faculty and would be thrilled to have you as colleagues. In sum, I am
+fully convinced that the University of Texas at Austin would be an
+incredible place to build my career.
+
+
+% a
+% My research and teaching can help bridge
+% between your experimentalists and 
+
+% the interests of members of the Division on either the experimental .
+
+
+
+% Professor Cummings' 
+
+% micro-level research and researchers such as
+% Professors Cummings and Prena and
+
+% Your Division has an interesting mi
+
+% My scholarship and interests connect to much work by members of your
+% division and I am interested in joining collaborations 
+
+
+% For instance,
+% Professor Katz' book on nudging for instance is deeply connected to my
+% interest in media technologies as power structures and to my empirical
+% research on how Wikipedia's flagging algorithms influenced moderator's
+% behavior. In a separate vein, Professor Prena's work on how people's
+% brains respond to video games interests me because it can speak to how
+% immersion in mediated environments can profoundly change us at a
+% cognitive level. Finally,
+% Professor Wells' work intersects with my interests in many ways, such
+% as in our efforts to study complex assemblages of interdependent media
+% and our interest in the role of identity-based groups in these
+% communication enologies.
+
+
+\vspace*{-0.5em}
+\closing{Sincerely, }
+\vspace*{-4em}
+\end{letter}
+
+% \textbf{Contact information of references}
+
+
+% Associate Professor Ceren Budak \\
+% \href{mailto:cbudak@umich.edu}{cbudak@umich.edu} \\
+% \href{tel:17347643341}{734-764-3341} 
+
+% \bigskip
+
+% Associate Professor Benjamin Mako Hill \\
+% \href{mailto:makohill@uw.edu}{makohill@uw.edu} \\
+% \href{tel:12064097191}{206-409-7191} 
+
+% \bigskip
+
+% Associate Professor Aaron Shaw \\
+% \href{mailto:aaronshaw@northwestern.edu}{aaronshaw@northwestern.edu} \\
+% \href{tel:6502699496}{650-269-9496}
+
+% \bigskip
+
+\end{document}
+
+% Ideally, projects give
+% students an opportunity to make their own knowledge about things important to them.
+% For instance, in my class on innovation communities, students identify problems they
+% face in their own lives which they not been able to solve. Problems have included feeling
+% safe late at night, having privacy on social media, and delivering home-cooked dumplings
+% without spoiling them. The students drew from and re-purposed strategies covered in
+% course material to successfully find unforeseen solutions. This approach improves learn-
+% ing because students will be motivated by their own needs to make concepts from class
+% concrete and actionable.
+
+
+% Mentoring individual students is deeply fulfilling to me. I am currently mentoring some-
+% one in the processes of applying to PhD programs on his qualitative research project study-
+% ing group dynamics in struggling teams in an online games. My approach as a mentor is
+% to be enthusiastic and encouraging before being negative, and to provide resources and
+% nudges before directing. I understand that many students, especially those new to re-
+% search or to working independently may not seek out the communication they may need
+% from their busy mentors. I make myself and my time easily available by co-working and
+% by participating in a group chat channel. I am sensitive to the many stresses inherent to
+% student life and always seek to place the student’s wellbeing first.
+
+
+\end{document}
+

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/letter/refs.bib

@@ -0,0 +1,216 @@
+@inproceedings{horta_ribeiro_automated_2023,
+  title = {Automated {{Content Moderation Increases Adherence}} to {{Community Guidelines}}},
+  booktitle = {Proceedings of the {{ACM Web Conference}} 2023},
+  author = {Horta Ribeiro, Manoel and Cheng, Justin and West, Robert},
+  year = {2023},
+  month = apr,
+  series = {{{WWW}} '23},
+  pages = {2666--2676},
+  publisher = {{Association for Computing Machinery}},
+  address = {{New York, NY, USA}},
+  urldate = {2023-05-18},
+  abstract = {Online social media platforms use automated moderation systems to remove or reduce the visibility of rule-breaking content. While previous work has documented the importance of manual content moderation, the effects of automated content moderation remain largely unknown. Here, in a large study of Facebook comments (n = 412M), we used a fuzzy regression discontinuity design to measure the impact of automated content moderation on subsequent rule-breaking behavior (number of comments hidden/deleted) and engagement (number of additional comments posted). We found that comment deletion decreased subsequent rule-breaking behavior in shorter threads (20 or fewer comments), even among other participants, suggesting that the intervention prevented conversations from derailing. Further, the effect of deletion on the affected user's subsequent rule-breaking behavior was longer-lived than its effect on reducing commenting in general, suggesting that users were deterred from rule-breaking but not from commenting. In contrast, hiding (rather than deleting) content had small and statistically insignificant effects. Our results suggest that automated content moderation increases adherence to community guidelines.},
+  isbn = {978-1-4503-9416-1},
+  keywords = {community guidelines,content moderation,online platforms},
+  file = {/home/nathante/Zotero/storage/V9A52WV3/Horta Ribeiro et al_2023_Automated Content Moderation Increases Adherence to Community Guidelines.pdf}
+}
+
+@article{hwang_why_2021,
+  title = {Why Do {{People Participate}} in {{Small Online Communities}}?},
+  author = {Hwang, Sohyeon and Foote, Jeremy},
+  year = {2021},
+  month = oct,
+  journal = {Proceedings of the ACM on Human-Computer Interaction},
+  volume = {5},
+  number = {CSCW2},
+  pages = {462:1--462:25},
+  urldate = {2022-02-23},
+  abstract = {Many benefits of online communities---such as obtaining new information, opportunities, and social connections---increase with size. Thus, a "successful'' online community often evokes an image of hundreds of thousands of users, and practitioners and researchers alike have sought to devise methods to achieve growth and thereby, success. On the other hand, small online communities exist in droves and many persist in their smallness over time. Turning to the highly popular discussion website Reddit, which is made up of hundreds of thousands of communities, we conducted a qualitative interview study examining how and why people participate in these persistently small communities, in order to understand why these communities exist when popular approaches would assume them to be failures. Drawing from twenty interviews, this paper makes several contributions: we describe how small communities provide unique informational and interactional spaces for participants, who are drawn by the hyperspecific aspects of the community; we find that small communities do not promote strong dyadic interpersonal relationships but rather promote group-based identity; and we highlight how participation in small communities is part of a broader, ongoing strategy to curate participants' online experience. We argue that online communities can be seen as nested niches: parts of an embedded, complex, symbiotic socio-informational ecosystem. We suggest ways that social computing research could benefit from more deliberate considerations of interdependence between diverse scales of online community sizes.},
+  keywords = {Computer Science - Human-Computer Interaction,Computer Science - Social and Information Networks,motivations,online communities,participation},
+  file = {/home/nathante/Zotero/storage/2RNY9QA6/Hwang and Foote - 2021 - Why do People Participate in Small Online Communit.pdf;/home/nathante/Zotero/storage/HHB6XSGX/Hwang and Foote - 2021 - Why do people participate in small online communit.pdf;/home/nathante/Zotero/storage/HU5Z6MWG/Hwang and Foote - 2021 - Why do people participate in small online communit.pdf}
+}
+
+@article{li_successful_2020,
+  title = {Successful {{Online Socialization}}: {{Lessons}} from the {{Wikipedia Education Program}}},
+  shorttitle = {Successful {{Online Socialization}}},
+  author = {Li, Ang and Yao, Zheng and Yang, Diyi and Kulkarni, Chinmay and Farzan, Rosta and Kraut, Robert E.},
+  year = {2020},
+  month = may,
+  journal = {Proceedings of the ACM on Human-Computer Interaction},
+  volume = {4},
+  number = {CSCW1},
+  pages = {50:1--50:24},
+  urldate = {2023-05-21},
+  abstract = {Attracting and retaining newcomers is critical and challenging for online production communities such as Wikipedia, both because volunteers need specialized training and are likely to leave before being integrated into the community. In response to these challenges, the Wikimedia Foundation started the Wiki Education Project (Wiki Ed), an online program in which college students edit Wikipedia articles as class assignments. The Wiki Ed program incorporates many components of institutional socialization, a process many conventional organizations successfully use to integrate new employees through formalized on-boarding practices. Research has not adequately investigated whether Wiki Ed and similar programs are effective ways to integrate volunteers in online communities, and, if so, the mechanisms involved. This paper evaluates the Wiki Ed program by comparing 16,819 student editors in 770 Wiki Ed classes with new editors who joined Wikipedia in the conventional way. The evaluation shows that the Wiki Ed students did more work, improved articles more, and were more committed to Wikipedia. For example, compared to new editors who joined Wikipedia in the conventional way they were twice as likely to still be editing Wikipedia a year after their Wiki Ed class was finished. Further, students in classrooms that encouraged joint activity, a key component of institutional socialization, produced better quality work than those in classrooms where students worked independently. These findings are consistent with an interpretation that the Wiki Ed program was successful because it incorporated elements of institutionalized socialization.},
+  keywords = {collective socialization,online production community,socialization},
+  file = {/home/nathante/Zotero/storage/QBKFTKBH/Li et al_2020_Successful Online Socialization.pdf}
+}
+
+@article{narayan_all_2019,
+  title = {All Talk: How Increasing Interpersonal Communication on Wikis May Not Enhance Productivity},
+  author = {Narayan, Sneha and TeBlunthuis, Nathan and Hale, Wm Salt and Hill, Benjamin Mako and Shaw, Aaron},
+  year = {2019},
+  month = nov,
+  journal = {Proceedings of the ACM: Human-Computer Interaction},
+  volume = {3},
+  number = {CSCW},
+  pages = {101:1-101:19},
+  langid = {english}
+}
+
+@unpublished{nathan_teblunthuis_automated_2023,
+  type = {In {{Submission}}},
+  title = {Automated {{Content Misclassification Causes Bias}} in {{Regression}}. {{Can We Fix It}}? {{Yes We Can}}!},
+  author = {{Nathan TeBlunthuis} and {Valerie Hase} and {Chung-Hong Chan}},
+  year = {2023}
+}
+
+@phdthesis{teblunthuis_density_2017-1,
+  type = {Master of {{Arts Thesis}}},
+  ids = {teblunthuis_density_2017-2,teblunthuis_density_2018},
+  title = {Density Dependence without Resource Partitioning on an Online Petitioning Platform},
+  author = {TeBlunthuis, Nathan},
+  year = {2017},
+  address = {{Seattle, Washington}},
+  urldate = {2019-12-15},
+  abstract = {Online petitions are a collective action tactic that leverages digital affordances in pursuit of discursive opportunities.  Prior efforts to explain why some petitions  are more successful than others emphasize signer motivations,  petition framing, social media, or resources from movement   organizations.  We advance a key insight of organizational ecology: population-level variables like density and concentration also   constrain success.  We use latent Dirichlet allocation (LDA) topic models to measure overlap density and frame specialization. We then model how ecological dynamics affect petition signature counts. We observe density dependence: a curvilinear relationship between overlap density and success.  We anticipated resource partitioning: specialists enjoy competitive advantages under concentration, but we find no evidence for it. We discuss boundary conditions for ecological dynamics commonly found in organizational fields induced by the distinctive scope of e-tactic platforms. Platforms may produce concentration without advantages for specialists by lowering entry costs for generalists and specialists alike.},
+  copyright = {CC BY},
+  langid = {american},
+  school = {University of Washington},
+  file = {/home/nathante/Zotero/storage/VBBQBQTL/TeBlunthuis_2017_Density dependence without resource partitioning on an online petitioning.pdf}
+}
+
+@article{teblunthuis_effects_2021-1,
+  title = {Effects of {{Algorithmic Flagging}} on {{Fairness}}: {{Quasi-experimental Evidence}} from {{Wikipedia}}},
+  shorttitle = {Effects of {{Algorithmic Flagging}} on {{Fairness}}},
+  author = {TeBlunthuis, Nathan and Hill, Benjamin Mako and Halfaker, Aaron},
+  year = {2021},
+  month = apr,
+  journal = {Proceedings of the ACM on Human-Computer Interaction},
+  volume = {5},
+  number = {CSCW1},
+  pages = {56:1--56:27},
+  urldate = {2021-09-21},
+  abstract = {Online community moderators often rely on social signals such as whether or not a user has an account or a profile page as clues that users may cause problems. Reliance on these clues can lead to "overprofiling'' bias when moderators focus on these signals but overlook the misbehavior of others. We propose that algorithmic flagging systems deployed to improve the efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible. We analyze moderator behavior in Wikipedia as mediated by RCFilters, a system which displays social signals and algorithmic flags, and estimate the causal effect of being flagged on moderator actions. We show that algorithmically flagged edits are reverted more often, especially those by established editors with positive social signals, and that flagging decreases the likelihood that moderation actions will be undone. Our results suggest that algorithmic flagging systems can lead to increased fairness in some contexts but that the relationship is complex and contingent.},
+  keywords = {ai,causal inference,community norms,fairness,machine learning,moderation,online communities,peer production,sociotechnical systems,wikipedia},
+  file = {/home/nathante/Zotero/storage/4JD44KTD/TeBlunthuis et al. - 2021 - Effects of Algorithmic Flagging on Fairness Quasi.pdf;/home/nathante/Zotero/storage/67CS6LL5/TeBlunthuis et al_2021_Effects of Algorithmic Flagging on Fairness.pdf;/home/nathante/Zotero/storage/6QQVCB5X/TeBlunthuis et al_2020_The effects of algorithmic flagging on fairness.pdf;/home/nathante/Zotero/storage/JDFFPFKH/TeBlunthuis et al. - 2021 - Effects of Algorithmic Flagging on Fairness Quasi.pdf;/home/nathante/Zotero/storage/HU4RGH9P/2006.html}
+}
+
+@inproceedings{teblunthuis_identifying_2022,
+  title = {Identifying Competition and Mutualism between Online Groups},
+  booktitle = {International {{AAAI Conference}} on {{Web}} and {{Social Media}} ({{ICWSM}} 2022)},
+  author = {TeBlunthuis, Nathan and Hill, Benjamin Mako},
+  year = {2022},
+  month = jun,
+  volume = {16},
+  pages = {993--1004},
+  publisher = {{AAAI}},
+  address = {{Atlanta, Georgia, USA}},
+  urldate = {2021-07-16},
+  abstract = {Platforms often host multiple online groups with highly overlapping topics and members. How can researchers and designers understand how interactions between related groups affect measures of group health? Inspired by population ecology, prior social computing research has studied competition and mutualism among related groups by correlating group size with degrees of overlap in content and membership. The resulting body of evidence is puzzling as overlaps seem sometimes to help and other times to hurt. We suggest that this confusion results from aggregating inter-group relationships into an overall environmental effect instead of focusing on networks of competition and mutualism among groups. We propose a theoretical framework based on community ecology and a method for inferring competitive and mutualistic interactions from time series participation data. We compare population and community ecology analyses of online community growth by analyzing clusters of subreddits with high user overlap but varying degrees of competition and mutualism.},
+  keywords = {Computer Science - Human-Computer Interaction,Computer Science - Social and Information Networks},
+  file = {/home/nathante/Zotero/storage/5TX4T4MH/TeBlunthuis_Hill_2021_Identifying Competition and Mutualism Between Online Groups.pdf;/home/nathante/Zotero/storage/E3FHWDYV/TeBlunthuis_Hill_2021_Identifying Competition and Mutualism Between Online Groups.pdf;/home/nathante/Zotero/storage/VBHBB5VB/TeBlunthuis and Hill - 2018 - A Community Ecology Approach for Identifying Compe.pdf;/home/nathante/Zotero/storage/CDP4AZQN/2107.html;/home/nathante/Zotero/storage/KK7SAIA6/2107.html}
+}
+
+@inproceedings{teblunthuis_measuring_2021,
+  title = {Measuring {{Wikipedia Article Quality}} in {{One Dimension}} by {{Extending ORES}} with {{Ordinal Regression}}},
+  booktitle = {17th {{International Symposium}} on {{Open Collaboration}}},
+  author = {Teblunthuis, Nathan},
+  year = {2021},
+  month = sep,
+  series = {{{OpenSym}} 2021},
+  pages = {1--10},
+  publisher = {{Association for Computing Machinery}},
+  address = {{New York, NY, USA}},
+  urldate = {2022-05-10},
+  abstract = {Organizing complex peer production projects and advancing scientific knowledge of open collaboration each depend on the ability to measure quality. Wikipedia community members and academic researchers have used article quality ratings for purposes like tracking knowledge gaps and studying how political polarization shapes collaboration. Even so, measuring quality presents many methodological challenges. The most widely used systems use quality assesements on discrete ordinal scales, but such labels can be inconvenient for statistics and machine learning. Prior work handles this by assuming that different levels of quality are ``evenly spaced'' from one another. This assumption runs counter to intuitions about degrees of effort needed to raise Wikipedia articles to different quality levels. I describe a technique extending the Wikimedia Foundations' ORES article quality model to address these limitations. My method uses weighted ordinal regression models to construct one-dimensional continuous measures of quality. While scores from my technique and from prior approaches are correlated, my approach improves accuracy for research datasets and provides evidence that the ``evenly spaced'' assumption is unfounded in practice on English Wikipedia. I conclude with recommendations for using quality scores in future research and include the full code, data, and models.},
+  isbn = {978-1-4503-8500-8},
+  keywords = {datasets,machine learning,measurement,methods,online communities,peer production,quality,sociotechnical systems,statistics,Wikipedia},
+  file = {/home/nathante/Zotero/storage/7XMM8HHR/Teblunthuis_2021_Measuring Wikipedia Article Quality in One Dimension by Extending ORES with.pdf}
+}
+
+@misc{teblunthuis_misclassification_2023,
+  title = {Misclassification in {{Automated Content Analysis Causes Bias}} in {{Regression}}. {{Can We Fix It}}? {{Yes We Can}}!},
+  shorttitle = {Misclassification in {{Automated Content Analysis Causes Bias}} in {{Regression}}. {{Can We Fix It}}?},
+  author = {TeBlunthuis, Nathan and Hase, Valerie and Chan, Chung-Hong},
+  year = {2023},
+  month = jul,
+  number = {arXiv:2307.06483},
+  eprint = {2307.06483},
+  primaryclass = {cs},
+  publisher = {{arXiv}},
+  urldate = {2023-08-30},
+  abstract = {Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video, and have become widely popular measurement devices in communication science and related fields. Despite this popularity, even highly accurate classifiers make errors that cause misclassification bias and misleading results in downstream analyses-unless such analyses account for these errors. As we show in a systematic literature review of SML applications, communication scholars largely ignore misclassification bias. In principle, existing statistical methods can use "gold standard" validation data, such as that created by human annotators, to correct misclassification bias and produce consistent estimates. We introduce and test such methods, including a new method we design and implement in the R package misclassificationmodels, via Monte Carlo simulations designed to reveal each method's limitations, which we also release. Based on our results, we recommend our new error correction method as it is versatile and efficient. In sum, automated classifiers, even those below common accuracy standards or making systematic misclassifications, can be useful for measurement with careful study design and appropriate error correction methods.},
+  archiveprefix = {arxiv},
+  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Computers and Society,Computer Science - Machine Learning,G.3,I.2.6,K.4.0},
+  file = {/home/nathante/Zotero/storage/EZB7F9TB/TeBlunthuis et al_2023_Misclassification in Automated Content Analysis Causes Bias in Regression.pdf;/home/nathante/Zotero/storage/EB952IIG/2307.html}
+}
+
+@article{teblunthuis_no_2022,
+  ids = {teblunthuis_no_2022-1},
+  title = {No {{Community Can Do Everything}}: {{Why People Participate}} in {{Similar Online Communities}}},
+  shorttitle = {No {{Community Can Do Everything}}},
+  author = {TeBlunthuis, Nathan and Kiene, Charles and Brown, Isabella and Levi, Laura (Alia) and McGinnis, Nicole and Hill, Benjamin Mako},
+  year = {2022},
+  month = apr,
+  journal = {Proceedings of the ACM on Human-Computer Interaction},
+  volume = {6},
+  number = {CSCW1},
+  pages = {61:1--61:25},
+  urldate = {2022-05-10},
+  abstract = {Large-scale quantitative analyses have shown that individuals frequently talk to each other about similar things in different online spaces. Why do these overlapping communities exist? We provide an answer grounded in the analysis of 20 interviews with active participants in clusters of highly related subreddits. Within a broad topical area, there are a diversity of benefits an online community can confer. These include (a) specific information and discussion, (b) socialization with similar others, and (c) attention from the largest possible audience. A single community cannot meet all three needs. Our findings suggest that topical areas within an online community platform tend to become populated by groups of specialized communities with diverse sizes, topical boundaries, and rules. Compared with any single community, such systems of overlapping communities are able to provide a greater range of benefits.},
+  keywords = {ecology,interviews,multiple communities,online communities,reddit},
+  file = {/home/nathante/Zotero/storage/EGS98FJB/TeBlunthuis et al_2022_No Community Can Do Everything.pdf;/home/nathante/Zotero/storage/L643SYXB/TeBlunthuis et al_2022_No Community Can Do Everything.pdf;/home/nathante/Zotero/storage/5T2YIRSX/2201.html}
+}
+
+@inproceedings{teblunthuis_revisiting_2018,
+  title = {Revisiting "{{The}} Rise and Decline" in a Population of Peer Production Projects},
+  booktitle = {Proceedings of the 2018 {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}} ({{CHI}} '18)},
+  author = {TeBlunthuis, Nathan and Shaw, Aaron and Hill, Benjamin Mako},
+  year = {2018},
+  pages = {355:1--355:7},
+  publisher = {{ACM}},
+  address = {{New York, NY}},
+  urldate = {2018-07-01},
+  abstract = {Do patterns of growth and stabilization found in large peer production systems such as Wikipedia occur in other communities? This study assesses the generalizability of Halfaker et al.'s influential 2013 paper on "The Rise and Decline of an Open Collaboration System." We replicate its tests of several theories related to newcomer retention and norm entrenchment using a dataset of hundreds of active peer production wikis from Wikia. We reproduce the subset of the findings from Halfaker and colleagues that we are able to test, comparing both the estimated signs and magnitudes of our models. Our results support the external validity of Halfaker et al.'s claims that quality control systems may limit the growth of peer production communities by deterring new contributors and that norms tend to become entrenched over time.},
+  isbn = {978-1-4503-5620-6},
+  keywords = {governance,online communities,peer production,quality control,replication,retention,wikipedia,wikis},
+  file = {/home/nathante/Zotero/storage/SJB2TQVS/TeBlunthuis et al_2018_Revisiting The rise and decline in a population of peer production projects.pdf}
+}
+
+@inproceedings{wang_how_2022,
+  title = {How Are {{ML-Based Online Content Moderation Systems Actually Used}}? {{Studying Community Size}}, {{Local Activity}}, and {{Disparate Treatment}}},
+  shorttitle = {How Are {{ML-Based Online Content Moderation Systems Actually Used}}?},
+  booktitle = {2022 {{ACM Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+  author = {Wang, Leijie and Zhu, Haiyi},
+  year = {2022},
+  month = jun,
+  series = {{{FAccT}} '22},
+  pages = {824--838},
+  publisher = {{Association for Computing Machinery}},
+  address = {{New York, NY, USA}},
+  urldate = {2023-05-18},
+  abstract = {Machine learning-based predictive systems are increasingly used to assist online groups and communities in various content moderation tasks. However, there are limited quantitative understandings of whether and how different groups and communities use such predictive systems differently according to their community characteristics. In this research, we conducted a field evaluation of how content moderation systems are used in 17 Wikipedia language communities. We found that 1) larger communities tend to use predictive systems to identify the most damaging edits, while smaller communities tend to use them to identify any edit that could be damaging; 2) predictive systems are used less in content areas where there are more local editing activities; 3) predictive systems have mixed effects on reducing disparate treatment between anonymous and registered editors across communities of different characteristics. Finally, we discuss the theoretical and practical implications for future human-centered moderation algorithms.},
+  isbn = {978-1-4503-9352-2},
+  keywords = {Causal inference,Content moderation,Fairness,Online communities,Wikipedia},
+  file = {/home/nathante/Zotero/storage/EQAPLTX2/Wang_Zhu_2022_How are ML-Based Online Content Moderation Systems Actually Used.pdf}
+}
+
+@article{zhu_content_2020,
+  ids = {zhu_content_2020-1},
+  title = {Content {{Growth}} and {{Attention Contagion}} in {{Information Networks}}: {{Addressing Information Poverty}} on {{Wikipedia}}},
+  shorttitle = {Content {{Growth}} and {{Attention Contagion}} in {{Information Networks}}},
+  author = {Zhu, Kai and Walker, Dylan and Muchnik, Lev},
+  year = {2020},
+  month = jun,
+  journal = {Information Systems Research},
+  volume = {31},
+  number = {2},
+  pages = {491--509},
+  publisher = {{INFORMS}},
+  issn = {1047-7047, 1526-5536},
+  urldate = {2020-08-31},
+  abstract = {Open collaboration platforms have fundamentally changed the way that knowledge is produced, disseminated, and consumed. In these systems, contributions arise organically with little to no central governance. Although such decentralization provides many benefits, a lack of broad oversight and coordination can leave questions of information poverty and skewness to the mercy of the system's natural dynamics. Unfortunately, we still lack a basic understanding of the dynamics at play in these systems and specifically, how contribution and attention interact and propagate through information networks. We leverage a large-scale natural experiment to study how exogenous content contributions to Wikipedia articles affect the attention that they attract and how that attention spills over to other articles in the network. Results reveal that exogenously added content leads to significant, substantial, and long-term increases in both content consumption and subsequent contributions. Furthermore, we find significant attention spillover to downstream hyperlinked articles. Through both analytical estimation and empirically informed simulation, we evaluate policies to harness this attention contagion to address the problem of information poverty and skewness. We find that harnessing attention contagion can lead to as much as a twofold increase in the total attention flow to clusters of disadvantaged articles. Our findings have important policy implications for open collaboration platforms and information networks.},
+  langid = {english},
+  file = {/home/nathante/Zotero/storage/URZHLULV/Zhu et al_2020_Content Growth and Attention Contagion in Information Networks.pdf;/home/nathante/Zotero/storage/QSILFXSA/isre.2019.html}
+}

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/research_statement/Makefile

@@ -0,0 +1,27 @@
+#!/usr/bin/make
+
+ENC_SIGIMAGE = figures/signature.pdf.gpg
+DEC_SIGIMAGE = figures/signature.pdf
+
+# to use encrypted signatures files, add "figures/signature.pdf" (no
+# quotes) right before the first $( in the following line:
+all: $(patsubst %.tex,%.pdf,$(wildcard *.tex))
+
+figures/signature.pdf:
+	gpg --yes --output $(DEC_SIGIMAGE) --decrypt $(ENC_SIGIMAGE)
+
+%.pdf: %.tex
+	latexmk -f -xelatex $<
+
+clean:
+	latexmk -C *.tex
+	$(RM) -f *.tmp *.run.xml
+	# to use encrypted signature files, uncomment the following line
+	# $(RM) -f $(DEC_SIGIMAGE)
+
+viewpdf: all
+	evince *.pdf
+
+pdf: all
+
+.PHONY: clean all update-sig

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/research_statement/refs.bib

@@ -0,0 +1,216 @@
+@inproceedings{horta_ribeiro_automated_2023,
+  title = {Automated {{Content Moderation Increases Adherence}} to {{Community Guidelines}}},
+  booktitle = {Proceedings of the {{ACM Web Conference}} 2023},
+  author = {Horta Ribeiro, Manoel and Cheng, Justin and West, Robert},
+  year = {2023},
+  month = apr,
+  series = {{{WWW}} '23},
+  pages = {2666--2676},
+  publisher = {{Association for Computing Machinery}},
+  address = {{New York, NY, USA}},
+  urldate = {2023-05-18},
+  abstract = {Online social media platforms use automated moderation systems to remove or reduce the visibility of rule-breaking content. While previous work has documented the importance of manual content moderation, the effects of automated content moderation remain largely unknown. Here, in a large study of Facebook comments (n = 412M), we used a fuzzy regression discontinuity design to measure the impact of automated content moderation on subsequent rule-breaking behavior (number of comments hidden/deleted) and engagement (number of additional comments posted). We found that comment deletion decreased subsequent rule-breaking behavior in shorter threads (20 or fewer comments), even among other participants, suggesting that the intervention prevented conversations from derailing. Further, the effect of deletion on the affected user's subsequent rule-breaking behavior was longer-lived than its effect on reducing commenting in general, suggesting that users were deterred from rule-breaking but not from commenting. In contrast, hiding (rather than deleting) content had small and statistically insignificant effects. Our results suggest that automated content moderation increases adherence to community guidelines.},
+  isbn = {978-1-4503-9416-1},
+  keywords = {community guidelines,content moderation,online platforms},
+  file = {/home/nathante/Zotero/storage/V9A52WV3/Horta Ribeiro et al_2023_Automated Content Moderation Increases Adherence to Community Guidelines.pdf}
+}
+
+@article{hwang_why_2021,
+  title = {Why Do {{People Participate}} in {{Small Online Communities}}?},
+  author = {Hwang, Sohyeon and Foote, Jeremy},
+  year = {2021},
+  month = oct,
+  journal = {Proceedings of the ACM on Human-Computer Interaction},
+  volume = {5},
+  number = {CSCW2},
+  pages = {462:1--462:25},
+  urldate = {2022-02-23},
+  abstract = {Many benefits of online communities---such as obtaining new information, opportunities, and social connections---increase with size. Thus, a "successful'' online community often evokes an image of hundreds of thousands of users, and practitioners and researchers alike have sought to devise methods to achieve growth and thereby, success. On the other hand, small online communities exist in droves and many persist in their smallness over time. Turning to the highly popular discussion website Reddit, which is made up of hundreds of thousands of communities, we conducted a qualitative interview study examining how and why people participate in these persistently small communities, in order to understand why these communities exist when popular approaches would assume them to be failures. Drawing from twenty interviews, this paper makes several contributions: we describe how small communities provide unique informational and interactional spaces for participants, who are drawn by the hyperspecific aspects of the community; we find that small communities do not promote strong dyadic interpersonal relationships but rather promote group-based identity; and we highlight how participation in small communities is part of a broader, ongoing strategy to curate participants' online experience. We argue that online communities can be seen as nested niches: parts of an embedded, complex, symbiotic socio-informational ecosystem. We suggest ways that social computing research could benefit from more deliberate considerations of interdependence between diverse scales of online community sizes.},
+  keywords = {Computer Science - Human-Computer Interaction,Computer Science - Social and Information Networks,motivations,online communities,participation},
+  file = {/home/nathante/Zotero/storage/2RNY9QA6/Hwang and Foote - 2021 - Why do People Participate in Small Online Communit.pdf;/home/nathante/Zotero/storage/HHB6XSGX/Hwang and Foote - 2021 - Why do people participate in small online communit.pdf;/home/nathante/Zotero/storage/HU5Z6MWG/Hwang and Foote - 2021 - Why do people participate in small online communit.pdf}
+}
+
+@article{li_successful_2020,
+  title = {Successful {{Online Socialization}}: {{Lessons}} from the {{Wikipedia Education Program}}},
+  shorttitle = {Successful {{Online Socialization}}},
+  author = {Li, Ang and Yao, Zheng and Yang, Diyi and Kulkarni, Chinmay and Farzan, Rosta and Kraut, Robert E.},
+  year = {2020},
+  month = may,
+  journal = {Proceedings of the ACM on Human-Computer Interaction},
+  volume = {4},
+  number = {CSCW1},
+  pages = {50:1--50:24},
+  urldate = {2023-05-21},
+  abstract = {Attracting and retaining newcomers is critical and challenging for online production communities such as Wikipedia, both because volunteers need specialized training and are likely to leave before being integrated into the community. In response to these challenges, the Wikimedia Foundation started the Wiki Education Project (Wiki Ed), an online program in which college students edit Wikipedia articles as class assignments. The Wiki Ed program incorporates many components of institutional socialization, a process many conventional organizations successfully use to integrate new employees through formalized on-boarding practices. Research has not adequately investigated whether Wiki Ed and similar programs are effective ways to integrate volunteers in online communities, and, if so, the mechanisms involved. This paper evaluates the Wiki Ed program by comparing 16,819 student editors in 770 Wiki Ed classes with new editors who joined Wikipedia in the conventional way. The evaluation shows that the Wiki Ed students did more work, improved articles more, and were more committed to Wikipedia. For example, compared to new editors who joined Wikipedia in the conventional way they were twice as likely to still be editing Wikipedia a year after their Wiki Ed class was finished. Further, students in classrooms that encouraged joint activity, a key component of institutional socialization, produced better quality work than those in classrooms where students worked independently. These findings are consistent with an interpretation that the Wiki Ed program was successful because it incorporated elements of institutionalized socialization.},
+  keywords = {collective socialization,online production community,socialization},
+  file = {/home/nathante/Zotero/storage/QBKFTKBH/Li et al_2020_Successful Online Socialization.pdf}
+}
+
+@article{narayan_all_2019,
+  title = {All Talk: How Increasing Interpersonal Communication on Wikis May Not Enhance Productivity},
+  author = {Narayan, Sneha and TeBlunthuis, Nathan and Hale, Wm Salt and Hill, Benjamin Mako and Shaw, Aaron},
+  year = {2019},
+  month = nov,
+  journal = {Proceedings of the ACM: Human-Computer Interaction},
+  volume = {3},
+  number = {CSCW},
+  pages = {101:1-101:19},
+  langid = {english}
+}
+
+@unpublished{nathan_teblunthuis_automated_2023,
+  type = {In {{Submission}}},
+  title = {Automated {{Content Misclassification Causes Bias}} in {{Regression}}. {{Can We Fix It}}? {{Yes We Can}}!},
+  author = {{Nathan TeBlunthuis} and {Valerie Hase} and {Chung-Hong Chan}},
+  year = {2023}
+}
+
+@phdthesis{teblunthuis_density_2017-1,
+  type = {Master of {{Arts Thesis}}},
+  ids = {teblunthuis_density_2017-2,teblunthuis_density_2018},
+  title = {Density Dependence without Resource Partitioning on an Online Petitioning Platform},
+  author = {TeBlunthuis, Nathan},
+  year = {2017},
+  address = {{Seattle, Washington}},
+  urldate = {2019-12-15},
+  abstract = {Online petitions are a collective action tactic that leverages digital affordances in pursuit of discursive opportunities.  Prior efforts to explain why some petitions  are more successful than others emphasize signer motivations,  petition framing, social media, or resources from movement   organizations.  We advance a key insight of organizational ecology: population-level variables like density and concentration also   constrain success.  We use latent Dirichlet allocation (LDA) topic models to measure overlap density and frame specialization. We then model how ecological dynamics affect petition signature counts. We observe density dependence: a curvilinear relationship between overlap density and success.  We anticipated resource partitioning: specialists enjoy competitive advantages under concentration, but we find no evidence for it. We discuss boundary conditions for ecological dynamics commonly found in organizational fields induced by the distinctive scope of e-tactic platforms. Platforms may produce concentration without advantages for specialists by lowering entry costs for generalists and specialists alike.},
+  copyright = {CC BY},
+  langid = {american},
+  school = {University of Washington},
+  file = {/home/nathante/Zotero/storage/VBBQBQTL/TeBlunthuis_2017_Density dependence without resource partitioning on an online petitioning.pdf}
+}
+
+@article{teblunthuis_effects_2021-1,
+  title = {Effects of {{Algorithmic Flagging}} on {{Fairness}}: {{Quasi-experimental Evidence}} from {{Wikipedia}}},
+  shorttitle = {Effects of {{Algorithmic Flagging}} on {{Fairness}}},
+  author = {TeBlunthuis, Nathan and Hill, Benjamin Mako and Halfaker, Aaron},
+  year = {2021},
+  month = apr,
+  journal = {Proceedings of the ACM on Human-Computer Interaction},
+  volume = {5},
+  number = {CSCW1},
+  pages = {56:1--56:27},
+  urldate = {2021-09-21},
+  abstract = {Online community moderators often rely on social signals such as whether or not a user has an account or a profile page as clues that users may cause problems. Reliance on these clues can lead to "overprofiling'' bias when moderators focus on these signals but overlook the misbehavior of others. We propose that algorithmic flagging systems deployed to improve the efficiency of moderation work can also make moderation actions more fair to these users by reducing reliance on social signals and making norm violations by everyone else more visible. We analyze moderator behavior in Wikipedia as mediated by RCFilters, a system which displays social signals and algorithmic flags, and estimate the causal effect of being flagged on moderator actions. We show that algorithmically flagged edits are reverted more often, especially those by established editors with positive social signals, and that flagging decreases the likelihood that moderation actions will be undone. Our results suggest that algorithmic flagging systems can lead to increased fairness in some contexts but that the relationship is complex and contingent.},
+  keywords = {ai,causal inference,community norms,fairness,machine learning,moderation,online communities,peer production,sociotechnical systems,wikipedia},
+  file = {/home/nathante/Zotero/storage/4JD44KTD/TeBlunthuis et al. - 2021 - Effects of Algorithmic Flagging on Fairness Quasi.pdf;/home/nathante/Zotero/storage/67CS6LL5/TeBlunthuis et al_2021_Effects of Algorithmic Flagging on Fairness.pdf;/home/nathante/Zotero/storage/6QQVCB5X/TeBlunthuis et al_2020_The effects of algorithmic flagging on fairness.pdf;/home/nathante/Zotero/storage/JDFFPFKH/TeBlunthuis et al. - 2021 - Effects of Algorithmic Flagging on Fairness Quasi.pdf;/home/nathante/Zotero/storage/HU4RGH9P/2006.html}
+}
+
+@inproceedings{teblunthuis_identifying_2022,
+  title = {Identifying Competition and Mutualism between Online Groups},
+  booktitle = {International {{AAAI Conference}} on {{Web}} and {{Social Media}} ({{ICWSM}} 2022)},
+  author = {TeBlunthuis, Nathan and Hill, Benjamin Mako},
+  year = {2022},
+  month = jun,
+  volume = {16},
+  pages = {993--1004},
+  publisher = {{AAAI}},
+  address = {{Atlanta, Georgia, USA}},
+  urldate = {2021-07-16},
+  abstract = {Platforms often host multiple online groups with highly overlapping topics and members. How can researchers and designers understand how interactions between related groups affect measures of group health? Inspired by population ecology, prior social computing research has studied competition and mutualism among related groups by correlating group size with degrees of overlap in content and membership. The resulting body of evidence is puzzling as overlaps seem sometimes to help and other times to hurt. We suggest that this confusion results from aggregating inter-group relationships into an overall environmental effect instead of focusing on networks of competition and mutualism among groups. We propose a theoretical framework based on community ecology and a method for inferring competitive and mutualistic interactions from time series participation data. We compare population and community ecology analyses of online community growth by analyzing clusters of subreddits with high user overlap but varying degrees of competition and mutualism.},
+  keywords = {Computer Science - Human-Computer Interaction,Computer Science - Social and Information Networks},
+  file = {/home/nathante/Zotero/storage/5TX4T4MH/TeBlunthuis_Hill_2021_Identifying Competition and Mutualism Between Online Groups.pdf;/home/nathante/Zotero/storage/E3FHWDYV/TeBlunthuis_Hill_2021_Identifying Competition and Mutualism Between Online Groups.pdf;/home/nathante/Zotero/storage/VBHBB5VB/TeBlunthuis and Hill - 2018 - A Community Ecology Approach for Identifying Compe.pdf;/home/nathante/Zotero/storage/CDP4AZQN/2107.html;/home/nathante/Zotero/storage/KK7SAIA6/2107.html}
+}
+
+@inproceedings{teblunthuis_measuring_2021,
+  title = {Measuring {{Wikipedia Article Quality}} in {{One Dimension}} by {{Extending ORES}} with {{Ordinal Regression}}},
+  booktitle = {17th {{International Symposium}} on {{Open Collaboration}}},
+  author = {Teblunthuis, Nathan},
+  year = {2021},
+  month = sep,
+  series = {{{OpenSym}} 2021},
+  pages = {1--10},
+  publisher = {{Association for Computing Machinery}},
+  address = {{New York, NY, USA}},
+  urldate = {2022-05-10},
+  abstract = {Organizing complex peer production projects and advancing scientific knowledge of open collaboration each depend on the ability to measure quality. Wikipedia community members and academic researchers have used article quality ratings for purposes like tracking knowledge gaps and studying how political polarization shapes collaboration. Even so, measuring quality presents many methodological challenges. The most widely used systems use quality assesements on discrete ordinal scales, but such labels can be inconvenient for statistics and machine learning. Prior work handles this by assuming that different levels of quality are ``evenly spaced'' from one another. This assumption runs counter to intuitions about degrees of effort needed to raise Wikipedia articles to different quality levels. I describe a technique extending the Wikimedia Foundations' ORES article quality model to address these limitations. My method uses weighted ordinal regression models to construct one-dimensional continuous measures of quality. While scores from my technique and from prior approaches are correlated, my approach improves accuracy for research datasets and provides evidence that the ``evenly spaced'' assumption is unfounded in practice on English Wikipedia. I conclude with recommendations for using quality scores in future research and include the full code, data, and models.},
+  isbn = {978-1-4503-8500-8},
+  keywords = {datasets,machine learning,measurement,methods,online communities,peer production,quality,sociotechnical systems,statistics,Wikipedia},
+  file = {/home/nathante/Zotero/storage/7XMM8HHR/Teblunthuis_2021_Measuring Wikipedia Article Quality in One Dimension by Extending ORES with.pdf}
+}
+
+@misc{teblunthuis_misclassification_2023,
+  title = {Misclassification in {{Automated Content Analysis Causes Bias}} in {{Regression}}. {{Can We Fix It}}? {{Yes We Can}}!},
+  shorttitle = {Misclassification in {{Automated Content Analysis Causes Bias}} in {{Regression}}. {{Can We Fix It}}?},
+  author = {TeBlunthuis, Nathan and Hase, Valerie and Chan, Chung-Hong},
+  year = {2023},
+  month = jul,
+  number = {arXiv:2307.06483},
+  eprint = {2307.06483},
+  primaryclass = {cs},
+  publisher = {{arXiv}},
+  urldate = {2023-08-30},
+  abstract = {Automated classifiers (ACs), often built via supervised machine learning (SML), can categorize large, statistically powerful samples of data ranging from text to images and video, and have become widely popular measurement devices in communication science and related fields. Despite this popularity, even highly accurate classifiers make errors that cause misclassification bias and misleading results in downstream analyses-unless such analyses account for these errors. As we show in a systematic literature review of SML applications, communication scholars largely ignore misclassification bias. In principle, existing statistical methods can use "gold standard" validation data, such as that created by human annotators, to correct misclassification bias and produce consistent estimates. We introduce and test such methods, including a new method we design and implement in the R package misclassificationmodels, via Monte Carlo simulations designed to reveal each method's limitations, which we also release. Based on our results, we recommend our new error correction method as it is versatile and efficient. In sum, automated classifiers, even those below common accuracy standards or making systematic misclassifications, can be useful for measurement with careful study design and appropriate error correction methods.},
+  archiveprefix = {arxiv},
+  keywords = {Computer Science - Artificial Intelligence,Computer Science - Computation and Language,Computer Science - Computers and Society,Computer Science - Machine Learning,G.3,I.2.6,K.4.0},
+  file = {/home/nathante/Zotero/storage/EZB7F9TB/TeBlunthuis et al_2023_Misclassification in Automated Content Analysis Causes Bias in Regression.pdf;/home/nathante/Zotero/storage/EB952IIG/2307.html}
+}
+
+@article{teblunthuis_no_2022,
+  ids = {teblunthuis_no_2022-1},
+  title = {No {{Community Can Do Everything}}: {{Why People Participate}} in {{Similar Online Communities}}},
+  shorttitle = {No {{Community Can Do Everything}}},
+  author = {TeBlunthuis, Nathan and Kiene, Charles and Brown, Isabella and Levi, Laura (Alia) and McGinnis, Nicole and Hill, Benjamin Mako},
+  year = {2022},
+  month = apr,
+  journal = {Proceedings of the ACM on Human-Computer Interaction},
+  volume = {6},
+  number = {CSCW1},
+  pages = {61:1--61:25},
+  urldate = {2022-05-10},
+  abstract = {Large-scale quantitative analyses have shown that individuals frequently talk to each other about similar things in different online spaces. Why do these overlapping communities exist? We provide an answer grounded in the analysis of 20 interviews with active participants in clusters of highly related subreddits. Within a broad topical area, there are a diversity of benefits an online community can confer. These include (a) specific information and discussion, (b) socialization with similar others, and (c) attention from the largest possible audience. A single community cannot meet all three needs. Our findings suggest that topical areas within an online community platform tend to become populated by groups of specialized communities with diverse sizes, topical boundaries, and rules. Compared with any single community, such systems of overlapping communities are able to provide a greater range of benefits.},
+  keywords = {ecology,interviews,multiple communities,online communities,reddit},
+  file = {/home/nathante/Zotero/storage/EGS98FJB/TeBlunthuis et al_2022_No Community Can Do Everything.pdf;/home/nathante/Zotero/storage/L643SYXB/TeBlunthuis et al_2022_No Community Can Do Everything.pdf;/home/nathante/Zotero/storage/5T2YIRSX/2201.html}
+}
+
+@inproceedings{teblunthuis_revisiting_2018,
+  title = {Revisiting "{{The}} Rise and Decline" in a Population of Peer Production Projects},
+  booktitle = {Proceedings of the 2018 {{CHI Conference}} on {{Human Factors}} in {{Computing Systems}} ({{CHI}} '18)},
+  author = {TeBlunthuis, Nathan and Shaw, Aaron and Hill, Benjamin Mako},
+  year = {2018},
+  pages = {355:1--355:7},
+  publisher = {{ACM}},
+  address = {{New York, NY}},
+  urldate = {2018-07-01},
+  abstract = {Do patterns of growth and stabilization found in large peer production systems such as Wikipedia occur in other communities? This study assesses the generalizability of Halfaker et al.'s influential 2013 paper on "The Rise and Decline of an Open Collaboration System." We replicate its tests of several theories related to newcomer retention and norm entrenchment using a dataset of hundreds of active peer production wikis from Wikia. We reproduce the subset of the findings from Halfaker and colleagues that we are able to test, comparing both the estimated signs and magnitudes of our models. Our results support the external validity of Halfaker et al.'s claims that quality control systems may limit the growth of peer production communities by deterring new contributors and that norms tend to become entrenched over time.},
+  isbn = {978-1-4503-5620-6},
+  keywords = {governance,online communities,peer production,quality control,replication,retention,wikipedia,wikis},
+  file = {/home/nathante/Zotero/storage/SJB2TQVS/TeBlunthuis et al_2018_Revisiting The rise and decline in a population of peer production projects.pdf}
+}
+
+@inproceedings{wang_how_2022,
+  title = {How Are {{ML-Based Online Content Moderation Systems Actually Used}}? {{Studying Community Size}}, {{Local Activity}}, and {{Disparate Treatment}}},
+  shorttitle = {How Are {{ML-Based Online Content Moderation Systems Actually Used}}?},
+  booktitle = {2022 {{ACM Conference}} on {{Fairness}}, {{Accountability}}, and {{Transparency}}},
+  author = {Wang, Leijie and Zhu, Haiyi},
+  year = {2022},
+  month = jun,
+  series = {{{FAccT}} '22},
+  pages = {824--838},
+  publisher = {{Association for Computing Machinery}},
+  address = {{New York, NY, USA}},
+  urldate = {2023-05-18},
+  abstract = {Machine learning-based predictive systems are increasingly used to assist online groups and communities in various content moderation tasks. However, there are limited quantitative understandings of whether and how different groups and communities use such predictive systems differently according to their community characteristics. In this research, we conducted a field evaluation of how content moderation systems are used in 17 Wikipedia language communities. We found that 1) larger communities tend to use predictive systems to identify the most damaging edits, while smaller communities tend to use them to identify any edit that could be damaging; 2) predictive systems are used less in content areas where there are more local editing activities; 3) predictive systems have mixed effects on reducing disparate treatment between anonymous and registered editors across communities of different characteristics. Finally, we discuss the theoretical and practical implications for future human-centered moderation algorithms.},
+  isbn = {978-1-4503-9352-2},
+  keywords = {Causal inference,Content moderation,Fairness,Online communities,Wikipedia},
+  file = {/home/nathante/Zotero/storage/EQAPLTX2/Wang_Zhu_2022_How are ML-Based Online Content Moderation Systems Actually Used.pdf}
+}
+
+@article{zhu_content_2020,
+  ids = {zhu_content_2020-1},
+  title = {Content {{Growth}} and {{Attention Contagion}} in {{Information Networks}}: {{Addressing Information Poverty}} on {{Wikipedia}}},
+  shorttitle = {Content {{Growth}} and {{Attention Contagion}} in {{Information Networks}}},
+  author = {Zhu, Kai and Walker, Dylan and Muchnik, Lev},
+  year = {2020},
+  month = jun,
+  journal = {Information Systems Research},
+  volume = {31},
+  number = {2},
+  pages = {491--509},
+  publisher = {{INFORMS}},
+  issn = {1047-7047, 1526-5536},
+  urldate = {2020-08-31},
+  abstract = {Open collaboration platforms have fundamentally changed the way that knowledge is produced, disseminated, and consumed. In these systems, contributions arise organically with little to no central governance. Although such decentralization provides many benefits, a lack of broad oversight and coordination can leave questions of information poverty and skewness to the mercy of the system's natural dynamics. Unfortunately, we still lack a basic understanding of the dynamics at play in these systems and specifically, how contribution and attention interact and propagate through information networks. We leverage a large-scale natural experiment to study how exogenous content contributions to Wikipedia articles affect the attention that they attract and how that attention spills over to other articles in the network. Results reveal that exogenously added content leads to significant, substantial, and long-term increases in both content consumption and subsequent contributions. Furthermore, we find significant attention spillover to downstream hyperlinked articles. Through both analytical estimation and empirically informed simulation, we evaluate policies to harness this attention contagion to address the problem of information poverty and skewness. We find that harnessing attention contagion can lead to as much as a twofold increase in the total attention flow to clusters of disadvantaged articles. Our findings have important policy implications for open collaboration platforms and information networks.},
+  langid = {english},
+  file = {/home/nathante/Zotero/storage/URZHLULV/Zhu et al_2020_Content Growth and Attention Contagion in Information Networks.pdf;/home/nathante/Zotero/storage/QSILFXSA/isre.2019.html}
+}

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/research_statement/research_statement.tex

@@ -0,0 +1,387 @@
+\documentclass[11pt,letterpaper]{article}
+
+% MODIFY THE DETAILS IN THIS SECTION TO MATCH WHAT YOU WANT ON /YOUR/ LETTERHEAD
+\def\department{Department of Communication}
+\def\uwaddress{Communications (CMU) 306~~Box 353740~~4109 NE Stevens Way~~Seattle, WA~~98195-3740}
+\def\phone{509-572-5081}
+\def\email{nathante@uw.edu}
+\def\web{https://teblunthuis.cc}
+
+\usepackage[top=1in,left=1in,bottom=1in,right=1in]{geometry}
+
+\usepackage{xltxtra}
+
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+\usepackage{polyglossia}
+\setdefaultlanguage{english}
+
+\usepackage{graphicx}
+\usepackage[colorlinks=false,
+            pdfborder={0 0 0},
+            ]{hyperref}
+\usepackage{tikz}
+\usepackage[natbib=true, style=numeric, backend=biber]{biblatex}
+\addbibresource{refs.bib}
+\DeclareLanguageMapping{american}{american-apa}
+
+\usepackage{lastpage}
+\usepackage{fancyhdr}
+\usepackage{titlesec}
+\pagestyle{fancy}
+\renewcommand{\footrulewidth}{0pt}
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+}
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+  \ifnum\thepage>1
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+\parindent 0pt
+\setlength{\parskip}{0.28\baselineskip}
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+% \raggedright
+\linespread{1.05}
+
+\title{Research Statement}
+\author{Nathan TeBlunthuis\\
+  \href{mailto:nathante@umich.edu}{nathante@umich.edu}} \date{}
+\hyphenation{wi-ki-pe-dia Wi-ki-pe-dia}
+
+\begin{document}
+\titlespacing{\section}{0pt}{1.5\parskip}{1.5\parskip}
+\titlespacing{\subsection}{0pt}{1.5\parskip}{1.5\parskip}
+\titlespacing{\subsubsection}{0pt}{1.5\parskip}{1.5\parskip}
+\maketitle
+
+Digital media have transformed not only how people receive and consume information but have enabled them to build new organizations and communication networks through which to achieve collective aims. This is exemplified by \textit{peer production} organizations that collaborate to create public information goods such as Wikipedia and open-source software or communities of activists who use digital media to build shared understandings of social problems and what to do about them. Although promising, such efforts to organize online face unique challenges. Most efforts to build such organizations fail to sustain long-lasting activity and even those that do face long-term problems related to sustainability and diversity.
+
+My scholarship seeks to understand the promises and pitfalls of such efforts and how technology can better support them. This is a very broad project and my work so far has two main ongoing focuses. The first earlier effort is on governance in peer production communities and the role of AI-based tools and status signals therein. The second focus responds to limitations of the first. Most explanations for success or failure looks at communities in isolation. However, many online communities or activism campaigns can happen at once and relationships among them can shape their processes and outcomes. Therefore, I use organizational ecology to understand how related efforts can coexist and the complex interactions among them.
+
+Below, I detail my prior studies in these lines of research. I will then describe methodological work I have done to solve problems related to measurement using machine learning models in computational social science. Finally, I will describe my plans for future research that will develop my two past focuses and address emerging socially important problems that result from recent disruptions to the digital media ecosystem. 
+
+\section*{Past Research}
+
+\subsection*{Governance and Design in Peer Production}
+
+One research question driving my scholarship is \emph{``How can
+  sociotechnical systems for quality control and interpersonal
+  communication help or hinder the growth and inclusivity of peer
+  production organizations?''} Although idealized as anarchic, Wikipedia's
+bureaucratic institutions for quality control are difficult for
+newcomers to navigate and are understood to limit its growth and
+diversity. Are such institutions unique to Wikipedia, an exceptionally
+complex project, or a generalizable pattern? My first study in this
+area found that patterns similar to Wikipedia's well-known
+rise-and-decline and associated barriers to newcomers were common
+among hundreds of smaller Wikis \citep{teblunthuis_revisiting_2018}.
+
+% Interpersonal communication with experienced contributors might help
+% newcomers to such projects socialize and learn the ropes. However,
+% even sending interpersonal messages on Wiki platforms has been a
+% challenge for newcomers because of obsolete design features. My
+% research evaluated an improved design for interpersonal messaging that
+% was adopted by hundreds of Wikis using an interrupted time-series
+% analysis to see if this improved newcomer retention
+% \citep{narayan_all_2019}. I found that the improved design lead to
+% increased interpersonal communication, but that this did not help
+% newcomers make more contributions --- often they made less. This study
+% helps show that introducing a design change can 
+
+Governance in peer production organizations and online communities
+often involves AI-based tools for monitoring and regulation. I
+investigated Wikipedia's machine learning algorithm predicting which
+Wikipedia edits were damaging and how it shaped the fairness of
+Wikipedia moderation. I have already summarized this work in my cover
+letter. 
+
+\subsection*{Ecological Approaches to Online Communities}
+
+The vast majority of studies of online organizations look at
+individual organizations in isolation and therefore suffer from
+important limitations related to generalization. All the studies
+described above address such limitations by pooling data from many
+organizations. However, they did not analyze how interactions among
+organizations may shape their development. To address this, I
+developed a new research program to answer the question: ``How do
+competitive struggles for resources and webs of symbiotic
+relationships among interdependent organizations shape their
+development?'' This program has been generously supported by the
+National Science Foundation through a graduate research fellowship
+(GRFP) in 2015 and a \$497,724 grant in 2019 that I played the primary
+role in conceptualizing and in which I am named personnel.
+
+Online organizations depart in important ways from organizational
+ecology's key assumptions about when related organizations will
+compete or be mutualists. Therefore, I set out to infer competition
+and mutualism networks directly from data on more than 4,000
+overlapping communities on the Reddit platform in work that received
+the top paper award from the Computational Methods Division at
+\emph{ICA 2021} and is published at \textit{ICWSM 2022}
+\cite{teblunthuis_identifying_2022}. In this project, I used a range
+of computational methods including document embedding, clustering, and
+time-series forecasting to measure the ecological dynamics among these
+online communities. The work's most important finding is that
+mutualism is much more common than competition among online
+communities with high user overlap. This is surprising because
+organizational ecologists usually find that highly overlapping
+organizations compete.
+
+To explain why people construct these overlapping organizations and
+why they are often mutualists, I led a team to interview their
+members. As reported in the resulting \emph{CSCW 2022} article, I
+found that widespread mutualism can arise because the different
+benefits that individuals obtain from these communities are often in
+tension. People might resolve these tensions by constructing complex
+organizations that attempt to provide the full range of benefits that
+individuals seek, but instead I found that they build several
+relatively simple communities, each specializing in a subset of
+benefits. The impact of this research is to show designers, users, and
+leaders of online organizations that related communities can coexist
+because a portfolio of specialized communities can better meet participants'
+diverse needs.
+
+% This leads people to build overlapping communities that specialize in providing different types of benefits. 
+% Make this more about organizational communication
+
+\subsection*{Machine Learning and Measurement}
+
+My efforts to measure ecological dynamics and the consequences of
+algorithmic moderation tools have convinced me of the importance of
+reliable measurement for progress in computational social science.
+Computational methods based on AI and machine learning have great
+potential as measurement devices, but their usefulness is limited by
+validity concerns that I am working to address in methodological
+projects. In one article, I show how calibrating a machine learning
+model improves the accuracy and interpretability of its measurement of
+Wikipedia article quality \citep{teblunthuis_measuring_2021}. Such
+problems are much broader than Wikipedia article quality measurement.
+Indeed, study designs that involve \textit{automated content
+  analysis}, in which automatic classifications by machine learning
+algorithms are input into statistical procedures are commonplace
+within computational social science. As my article under review at
+\emph{Communication Methods and Measures} with a ``minor revision''
+decision shows, such study designs are prone to result in misleading
+inferences, even when classifiers meet commonly held accuracy
+standards \citep{teblunthuis_misclassification_2023}. The good news is
+that statistical methods exist that can use a sample of validation
+data to fix the problem. My article evaluates such methods and
+introduces one from biostatistics that works in a wide range of
+studies including when classifiers are biased in ways that correlate
+with other variables in the analyses.
+
+\section*{Future Research}
+
+\subsection*{Digital Media Ecosystems}
+
+I have a number of ongoing projects about digital media ecosystems
+that I am preparing for publication.
+
+\subsubsection*{Online Organizational Ecology}
+I am preparing two new articles on the ecology of online communities.
+The first, in preparation for submission to \emph{Nature Human
+  Behavior}, received the Computational Methods Division's top paper
+award at \emph{ICA 2022} and uses nonlinear time series models drawn
+from mathematical ecology to study how ecological interactions change
+over time and to test adaptation and selection processes by which an
+ecology of mutualistic online communities may emerge. The second
+project develops a theory of ecology of online groups by synthesizing
+empirical results on interdependence among online communities with
+theories of organizational ecology and media niches. This article 
+was presented to the Organizational Communication Division
+at \emph{NCA 2022} and is in preparation for submission to
+a management journal.
+
+\subsubsection*{Ecology of Digital Activism}
+
+My work-in-progress ``Density-Dependent Competition in Online
+Petitions,'' will soon be submitted to a top general science journal.
+In this study, I use topic models and fixed effects regression
+analysis to investigate ecological dynamics among over 50,000 online
+petitions on Change.org and find that competition among similar
+concurrent online petitions can be intense and that specialization is
+at best a limited strategy to escape it. This finding, in contrast
+with the frequent mutualism I observed among active online communities
+on Reddit, helps show how online platforms provide institutional
+frameworks that shape whether related organizing efforts are
+mutualistic or competitive.
+
+In my current postdoc, I am developing new research into how labor
+movement activists use different social media platforms that uses an
+ecological lens. In contrast to positive accounts in scholarship on
+movements pursuing justice for marginalized individuals (e.g., BLM,
+LGBTQ+, \#metoo), labor movement practice and scholarship has been
+ambivalent about social media's utility. Institutional unions'
+traditional communication practices are top-down and internally
+focused, but social media channels let labor activists reach broader
+audiences of supporters by following strategies akin to those used by
+other social movements. Despite its long decline, the labor movement
+is currently projecting renewed strength through strikes, assertive
+negotiations, and the formation of ``retail unions'' such as those
+representing Starbucks coffee shop workers. In this project I ask:
+``How do labor activists in different types of unions use social
+media?'' and ``How do different labor campaigns relate to each
+other?'' To answer it I am building on my methodological work above
+for obtaining valid statistical inferences using automatic
+classifications, using large language models for automatic
+classification and conducting cross-platform ecological analyses.
+
+\subsection*{Peer Production Organizations}
+
+Peer production organizations like Wikipedia and those building open
+source software are economically important not only
+because of their value to technology companies and their creation of
+public goods. They also innovate organizing processes and
+technological practices such as those related to knowledge-bases,
+peripheral participation, flat organizations, open communication, and
+version control systems that have spilled over into other
+organizations and industries. In my future research, I will study the
+history of innovation within these organizational fields and how these
+organizations manage the emerging threats and opportunities of
+low-cost AI-generated content.
+
+\subsubsection*{Solving collective action problems}
+
+A major theoretical puzzle related to peer production is to explain
+how organizations can create conditions where many people volunteer
+when it requires considerable effort, difficult-to-acquire skills, or
+conflict with other volunteers. This is an instance of the problem of
+collective action. Available explanations generally look to
+volunteers' heterogeneous motivations such as altruism or indirect
+incentives. At the same time, these organizations' effectiveness is
+attributable to their distinctive cultures e.g., the ideologies,
+technological practices, and communication norms that enable
+cooperative work among these heterogeneous volunteers. In my first
+future research direction, I will ask ``how do such cultural features
+help peer production organizations solve collective action
+problems and how did they form?''
+
+I plan to link collective action problems to innovations in
+organizational practices using long-term and fine-grained data on
+communication and production activities in real-world peer-production
+organizations (Wikipedia editors, open street map cartographers, and
+Debian operating system developers). To identify collective action
+problems, I plan to empirically instantiate a theoretical framework in
+organizational sociology by Heckathorn that links the shape of
+production functions (quantifying much value is produced through
+someone's marginal effort) to types of problems related to
+cooperation, under-production, or conflict that cause productive
+activity to stall. This way, I will test predictions of Heckathorn's
+theory that which types of game-theoretic situations arise depend on
+the shape and level of the production function.
+
+Next, I will trace the organizational changes these organizations made
+(or attempted) in response to the problems by using data on the
+historical policies, norms, and discourses of these organizations
+together with behavioral traces of how they have enforced these
+policies and norms. I will also look for changes in their
+technological practice as when new tools are adopted. Then, I will
+test whether these innovations helped the organization improve
+performance in collective action games and accelerated their
+production functions.
+
+This plan relies on methods of computational social science in several
+ways. For instance, I will use machine learning classifiers to
+quantify value in measuring production functions, when types of
+problems occur, and when norms or policies are invoked. I will also
+use big data technologies to construct analytic datasets from
+unstructured data obtained via mailing lists, web archives, and git
+repositories.
+
+\subsubsection*{Managing threats of AI-generated content}
+
+The second major research direction I am planning asks: ``How does
+AI-generated content threaten projects such as Wikipedia and Stack
+Exchange and how should these projects respond?''\footnote{I have some
+  concerns with the term ``AI'', but use it for convenience.} 
+A dilemma has sparked considerable alarm and debate among information
+goods producers: GAI technologies promise to usefully automate or
+assist in tasks such as summarization, copy writing, translation, and
+classification, but they may also compete with or disrupt the social
+and communication processes that underpin coordination and cooperation
+in communities such as Wikipedia, Stack Exchange, and Reddit. I plan
+to write a new proposal to a program such as Human-Centered Computing
+at the National Science Foundation or Digital Infrastructure Insights
+at the Ford Foundation to support this work. These ideas are
+developing in the direction of three specific questions: (1) How will
+competition between GAI companies and online communities reshape
+digital ecosystems? (2) What promises or threats does GAI pose the
+internal organizing processes of these communities? (3) What are the
+consequences of steps communities take to use GAI or safeguard
+themselves from the above threats?
+
+% These questions are important as communities and platforms design
+% GAI's new role in the digital environment and adapt to it. 
+
+GAI's competitive threat has already reshaped the digital ecosystem.
+In part to protect its data from appropriation by competitors,
+Reddit.com, host of many communities producing knowledge bases and
+networks of experts, instituted unprecedented limits to the technical
+interfaces for interacting with its platform. Reddit communities
+protested that this change damaged their relationship with the
+platform and would make their work more difficult, and some ultimately
+migrated to alternative platforms. Research into such cases can
+contribute new understandings to organizational communication and CSCW
+of how technological changes that affect relationships between actors
+at different levels (i.e., the platform and community) can in turn
+reconfigure complex information ecosystems.
+
+On the other hand, Wikipedia has long used AI technologies for quality
+control and content translation, and (with important caveats) is
+relatively open to GAI content compared to many Stack Exchange
+question-answering communities, which have prohibited it. However, GAI
+may threaten Wikipedia's essential systems of trust that require
+editors to build reputation by contributing to encyclopedia content
+and helping to organize the project. This takes a lot of work, but
+motivated actors can create false but reputable identities called
+\emph{sockpuppets}. For example, a small group used a sockpuppet for
+over a decade in their apparently coordinated efforts to include
+antisemitic narratives and false claims about the Holocaust in
+Wikipedia articles as recent scholarship has documented. Although GAI may
+make building sockpuppets easier, potential safeguards such as
+stronger identity requirements may deter contributions from newcomers
+and others.  Comparing how Wikipedia and Stack Exchange
+approach such dilemmas and evaluating their policy and technical
+interventions will support theoretical contributions to computer
+mediated communication about how people build the trust they need to
+self-organize in a digital environment when GAI tools are widespread.
+
+Despite such threats, responding to AI-generated content bears its own
+risks. Steps that peer-producers take to promote quality and knowledge
+integrity can harm the new, peripheral, and diverse contributors who
+are vital to sustaining these organizations. These debates are muddied
+by empirical uncertainty about AI-generated content, its resulting
+harms, and the consequences of steps communities may take in response.
+
+I plan to study such threats and how they should be mitigated. I am
+prepared to solve the challenges involved by building on my
+methodological work using machine learning for measurement and on my
+past work evaluating designs for Wikipedia governance systems. In
+addition, I plan to use information security scholarship to help me
+model and detect misuse of AI-generated content. This work's impact
+will help address an important problem faced by widely trusted
+information sources and generate new understandings of how people
+build trust in each other through digital media in the age of
+generative AI that they need to collaborate.
+
+\printbibliography
+
+\end{document} 
+

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/teaching_statement/Makefile

@@ -0,0 +1,27 @@
+#!/usr/bin/make
+
+ENC_SIGIMAGE = figures/signature.pdf.gpg
+DEC_SIGIMAGE = figures/signature.pdf
+
+# to use encrypted signatures files, add "figures/signature.pdf" (no
+# quotes) right before the first $( in the following line:
+all: $(patsubst %.tex,%.pdf,$(wildcard *.tex))
+
+figures/signature.pdf:
+	gpg --yes --output $(DEC_SIGIMAGE) --decrypt $(ENC_SIGIMAGE)
+
+%.pdf: %.tex
+	latexmk -f -xelatex $<
+
+clean:
+	latexmk -C *.tex
+	$(RM) -f *.tmp *.run.xml
+	# to use encrypted signature files, uncomment the following line
+	# $(RM) -f $(DEC_SIGIMAGE)
+
+viewpdf: all
+	evince *.pdf
+
+pdf: all
+
+.PHONY: clean all update-sig

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/teaching_statement/auto/letter.el

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+(TeX-add-style-hook
+ "letter"
+ (lambda ()
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+   (TeX-run-style-hooks
+    "latex2e"
+    "article"
+    "art11"
+    "geometry"
+    "xltxtra"
+    "fontspec"
+    "polyglossia"
+    "graphicx"
+    "hyperref"
+    "tikz"
+    "lastpage"
+    "fancyhdr")
+   (TeX-add-symbols
+    "department"
+    "uwaddress"
+    "phone"
+    "email"
+    "web"))
+ :latex)
+

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/teaching_statement/auto/research_statement.el

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+(TeX-add-style-hook
+ "research_statement"
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+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "href")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "hyperref")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "hyperimage")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "hyperbaseurl")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "nolinkurl")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "url")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "path")
+   (add-to-list 'LaTeX-verbatim-macros-with-delims-local "path")
+   (TeX-run-style-hooks
+    "latex2e"
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+   (TeX-add-symbols
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+   (LaTeX-add-polyglossia-langs
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+ :latex)
+

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/teaching_statement/auto/teaching_statement.el

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+(TeX-add-style-hook
+ "teaching_statement"
+ (lambda ()
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+   (TeX-add-to-alist 'LaTeX-provided-package-options
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+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "href")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "hyperref")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "hyperimage")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "hyperbaseurl")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "nolinkurl")
+   (add-to-list 'LaTeX-verbatim-macros-with-braces-local "url")
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+   (LaTeX-add-polyglossia-langs
+    '("english" "defaultlanguage" "")))
+ :latex)
+

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/teaching_statement/teaching_statement.tex

@@ -0,0 +1,206 @@
+\documentclass[12pt,letterpaper]{article}
+
+% MODIFY THE DETAILS IN THIS SECTION TO MATCH WHAT YOU WANT ON /YOUR/ LETTERHEAD
+\def\department{Department of Communication}
+\def\uwaddress{Communications (CMU) 306~~Box 353740~~4109 NE Stevens Way~~Seattle, WA~~98195-3740}
+\def\phone{509-572-5081}
+\def\email{nathante@uw.edu}
+\def\web{https://teblunthuis.cc}
+
+\usepackage[top=1.25in,left=1.25in,bottom=1.20in,right=1.25in]{geometry}
+
+\usepackage{xltxtra}
+
+\usepackage{fontspec}
+\setsansfont{OpenSans}[
+Path = ./fonts/OpenSans/,
+Extension = .ttf,
+UprightFont     =   *-Regular,
+BoldFont        =   *-Bold,
+ItalicFont      =   *-Italic,
+BoldItalicFont  =   *-BoldItalic
+]
+
+\setmainfont{OpenSans}
+ 
+\usepackage{polyglossia}
+\setdefaultlanguage{english}
+
+\usepackage{graphicx}
+\usepackage[colorlinks=false,
+            pdfborder={0 0 0},
+            ]{hyperref}
+\usepackage{tikz}
+
+\usepackage{lastpage}
+\usepackage{fancyhdr}
+\pagestyle{fancy}
+\renewcommand{\footrulewidth}{0pt}
+\renewcommand{\headrulewidth}{0pt}
+\fancyhead{}
+\fancyhead[C]{%
+  \ifnum\thepage=1
+  \begin{tikzpicture}[remember picture,overlay,every node/.style={inner sep=0,outer sep=0}]
+    \node at (current page.north west) [anchor=north west]
+    {\includegraphics{figures/matrix_header.pdf}};
+    % add the department name
+    \node at (current page.north west) [anchor=north west, xshift=1.19in, yshift=-0.56in]
+    {{\fontsize{14pt}{16pt} \fontspec{MatrixIIOT-Book.otf}[
+        Path = ./fonts/Matrix/
+        ] \MakeUppercase \department}};
+  \end{tikzpicture}
+  \fi
+}
+\fancyhead[R]{%
+  \ifnum\thepage>1
+  Page \thepage%/\pageref*{LastPage}
+  \fi
+}
+\fancyfoot{}
+\fancyfoot[C]{%
+  \ifnum\thepage=1
+  \begin{tikzpicture}[remember picture,overlay,every node/.style={inner sep=0,outer sep=0}]
+    \node at (current page.south west) [yshift=0.614in,xshift=1.21in,anchor=north west]
+      {\sffamily \fontsize{9pt}{11pt} \selectfont \uwaddress};
+    \node at (current page.south west) [yshift=0.40in,xshift=1.21in,anchor=north west]
+      {\sffamily \fontsize{9pt}{11pt} \selectfont \phone~~\email~~\web};
+  \end{tikzpicture}
+  \fi
+}
+\fancyfoot[L] {}
+\fancyfoot[R] {}
+
+% No paragraph indentation
+\parindent 0pt
+
+\setlength{\parskip}{0.5\baselineskip}
+\setlength{\headheight}{14pt}
+\setlength{\footskip}{35pt}
+% \raggedright
+\linespread{1.05}
+
+\title{Teaching Statement}
+\author{Nathan TeBlunthuis}
+
+\date{}
+
+\begin{document}
+
+\maketitle
+
+% Big idea / big puzzle goes here.
+% Would Wikipedia be one of the most visited websites in the world if other online collaborative encyclopedia projects had been more established when it was founded? Or was Wikipedia helped by the fact that its predecessors had engaged and trained hundreds of its future contributors? Do new discussion communities on Reddit compete with existing communities for contributors? 
+% How does the environment of existing online communities shape the growth, performance, and impact of new groups?
+
+If I join the School of Information at the University of Texas at
+Austin, I would like to teach beginning and advanced courses on social
+informatics and human-centered data science such as ``Introduction to
+Human-Centered Data Science'', ``Introduction to Social Informatics'',
+``Statistics for Informatics'', and ``Data Engineering''. I would also
+love to develop new graduate-level curriculum on computational social
+science. I am qualified to do so thanks to my extensive experience
+with data science methods, big data technologies, and scientific
+communication in industrial, nonprofit, and academic settings and
+because of my experience teaching introductory programming and data
+science in Python as a mentor and instructor of the Community Data
+Science Workshops (CDSW) at the University of Washington. I am also
+prepared and eager to teach on organizational communication and new
+communication technologies.
+
+I have taught, as the instructor of record, ``Interpersonal Media:
+Online Communities'', a course applying social psychology,
+communication theory, and human-computer interaction scholarship to
+the design of online communities. I would like build on this as
+a new special topics course in user experience design. My experience
+also includes assistant teaching ``Innovation Communities'', a
+master's level course on communication strategies that organizations
+use to innovate. This course was taught using case-based learning in a
+style similar to many MBA courses. I have also taught, as a teaching
+assistant, ``Organizational Communication''. This course heavily used
+active learning pedagogy techniques and involved projects in which
+students conducted interviews and participant observation in
+real-world organizations.
+
+My teaching philosophy reflects my commitment to collaborative
+knowledge production that motivates my research and engagement with
+communities such as Wikipedia. My courses are built around
+project-based learning, and active or case-based learning. Students
+undertake 1-2 substantive projects that often result in contributions
+to public goods, such as the construction of public artifacts like
+Wikipedia articles, or improvements to the moderation policies and
+practices of an online community. In my organizational communication
+class, students produce reports that analyze and give recommendations
+to local organizations. These projects create unique opportunities for
+students to achieve and demonstrate mastery of knowledge as they
+investigate solve real problems in the world outside the classroom. I
+am committed to doing this even in larger classes and have found
+creative ways to scale this approach in order to do so. For instance,
+using an inverted classroom lets me use class time for in-class
+discussion, cases, and small-group activities that scaffold the
+projects and provide personalized support to students.
+
+I value the unique opportunities that projects create for students to
+achieve and demonstrate mastery of knowledge. For example, my class on
+online communities is built around two projects. In the first project,
+students learn the ropes of Wikipedia and make substantive
+contributions to an article of their choice. When learners choose to
+work on Wikipedia articles of their intrinsic interests, such as their
+hometown ski hill, favorite Twitch streamer or local rapper, their
+project becomes less about completing an assignment and more about
+contributing to a community they care about. Undergraduate students
+with limited experience working in formal organizations are often
+familiar with online communities and can recognize course concepts
+such as bureaucratic rationality, social norms, technological
+affordances, and interaction networks. In such projects, students
+interact with community members and experience first-hand why such
+concepts are important in organizational practice.
+
+I acknowledge that my experience, especially in recent years, has
+involved more research than teaching. I look forward to new opportunities
+to dedicate myself to pedagogy and develop as an educator.
+
+% An important part of my mission in teaching and mentorship is to advance diveristy, equity, and inclusion ( DEI) within the academy and throughout society. Most immediately, I recognize that the labor of mentoring people from underrepresented backgrounds too often falls disproportionately on faculty from such backgrounds, but I  The majority of my teaching and mentoring to date has involved working with women or people of color. In graduate school, I mentored three undergraduates, all women communication majors who successfully authored a paper published in CSCW, a journal of the Association for Computing Machinery (ACM).
+% % I  research led by a Black woman who is a graduate student at Purdue University and who presented our paper at \emph{ICA 2022}.
+% I am also currently mentoring three other graduate students of color on new projects and have helped recruit two students from  underrepresented backgrounds in computational social science to the communication Ph.D. program at the University of Washington. If I join the faculty of the Work and Organization Studies group, I will redouble these efforts. My contributions to DEIare not limited to teaching and mentorship. I recognize that DEI problems are systemic 
+
+
+%Similarly, in the ``Organizational Communication'' class that I taught as a teaching assistant, students undertook projects where they interviewed workers ranging from department store buyers to boxing gym owners and analyzed their organizations in terms of organizational communication theories.
+
+% One of my core pedagogical goals is to help students become critical consumers of information.  
+% Projects that take students beyond the classroom inevitably reveal the ways in which the knowledge presented in the course material is incomplete.
+% Therefore, I invite students to critically reflect on how the readings and lectures did not fully prepare them for real-world problems or explain their experiences.
+% These reflections inform how I design and teach future iterations of the class by pointing me to the limitations of textbook approaches and helping me consider new ideas.
+% They also provide an opportunity to demonstrate to students that knowledge is always in a construction process in which they can become active participants.
+
+
+
+% Sincerely,\\
+% Nathan TeBlunthuis\\
+% Department of Communication\\
+% University of Washington
+
+
+% Ideally, projects give
+% students an opportunity to make their own knowledge about things important to them.
+% For instance, in my class on innovation communities, students identify problems they
+% face in their own lives which they not been able to solve. Problems have included feeling
+% safe late at night, having privacy on social media, and delivering home-cooked dumplings
+% without spoiling them. The students drew from and re-purposed strategies covered in
+% course material to successfully find unforeseen solutions. This approach improves learn-
+% ing because students will be motivated by their own needs to make concepts from class
+% concrete and actionable.
+
+
+% Mentoring individual students is deeply fulfilling to me. I am currently mentoring some-
+% one in the processes of applying to PhD programs on his qualitative research project study-
+% ing group dynamics in struggling teams in an online games. My approach as a mentor is
+% to be enthusiastic and encouraging before being negative, and to provide resources and
+% nudges before directing. I understand that many students, especially those new to re-
+% search or to working independently may not seek out the communication they may need
+% from their busy mentors. I make myself and my time easily available by co-working and
+% by participating in a group chat channel. I am sensitive to the many stresses inherent to
+% student life and always seek to place the student’s wellbeing first.
+
+
+\end{document}
+