replace junk letter with just the pdf.

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/letter/Makefile

@@ -1,27 +0,0 @@
-#!/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

@@ -1,39 +0,0 @@
-(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

@@ -1,338 +0,0 @@
-\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/letter/structure.tex

@@ -1,95 +0,0 @@
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-% Professional Formal Letter
-% Structure Specification File
-% Version 1.0 (12/2/17)
-%
-% This file originates from:
-% http://www.LaTeXTemplates.com
-%
-% Authors:
-% Brian Moses
-% Vel (vel@LaTeXTemplates.com)
-%
-% License:
-% CC BY-NC-SA 3.0 (http://creativecommons.org/licenses/by-nc-sa/3.0/)
-%
-% Updated by: Aaron Shaw, 2018
-%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-%----------------------------------------------------------------------------------------
-%	PACKAGES AND OTHER DOCUMENT CONFIGURATIONS
-%----------------------------------------------------------------------------------------
-
-\usepackage{microtype} % Improves typography
-
-\pagestyle{empty} % Suppress headers and footers
-
-%\setlength\parindent{1cm} % Paragraph indentation
-
-%----------------------------------------------------------------------------------------
-%	DEFINE CUSTOM COMMANDS
-%----------------------------------------------------------------------------------------
-
-\newcommand{\logo}[1]{\renewcommand{\logo}{#1}}
-
-\newcommand{\Who}[1]{\renewcommand{\Who}{#1}}
-\newcommand{\Title}[1]{\renewcommand{\Title}{#1}}
-\newcommand{\email}[1]{\renewcommand{\email}{#1}}
-
-\newcommand{\authordetails}[1]{\renewcommand{\authordetails}{#1}}
-
-%----------------------------------------------------------------------------------------
-%	AUTHOR DETAILS STRUCTURE
-%----------------------------------------------------------------------------------------
-
-\newcommand{\authordetailsblock}{
-	\hspace{\fill} % Move the author details to the far right
-	\parbox[t]{0.48\textwidth}{ % Box holding the author details; width value specifies where it starts and ends, increase to move details left
-		\footnotesize % Use a smaller font size for the details
-                \raggedleft
-%		\textcolor{Gray}{\Who}\\ % Author name
-		\textcolor{Gray}{\authordetails} % The author details text, all italicised
-	}
-}
-
-%----------------------------------------------------------------------------------------
-%	HEADER STRUCTURE
-%----------------------------------------------------------------------------------------
-%
-%\address{
-%        \hfill\\~\\[-0.11\textheight] % Reduce the whitespace above %authordetails
-%        \authordetailsblock % Include the letter author's details on
-%                            % the right side of the page
-%%	\hspace{-0.25\textwidth} % Horizontal position of the author details %block, increase to move left, decrease to move right
-%}
-
-%----------------------------------------------------------------------------------------
-%	COMPOSE THE ENTIRE HEADER
-%----------------------------------------------------------------------------------------
-
-\renewcommand{\opening}[1]{
-%	{\centering\fromaddress\vspace{0.03\textheight}} % Print the header and from address here, add whitespace to move date down
-
-%        {\raggedright \par \toname \toaddress \par} % Print the to name and address
-	\vspace{3em} % White space after the to address
-	\noindent #1 % Print the opening line
-}
-
-%----------------------------------------------------------------------------------------
-%	SIGNATURE STRUCTURE
-%----------------------------------------------------------------------------------------
-
-% Currently ignored
-\signature{\Who\Title} % The signature is a combination of the author's name and title
-
-\renewcommand{\closing}[1]{
-	\vspace{2.5mm} % Some whitespace after the letter content and before the signature
-	\noindent % Stop paragraph indentation
-%	\hspace*{\longindentation} % Move the signature right to the value of \longindentation
-	\parbox{\indentedwidth}{
-		\raggedright
-		#1 % Print the signature text
-		\vskip 1.65cm % Whitespace between the closing text and author's name for a physical signature
-		\fromsig % Prints the value of \signature{}, i.e. author name and title
-	}
-}

job_application_materials/faculty_jobs/teblunthuis_ut_soi_2023/letter/refs.bib

@@ -1,216 +0,0 @@
-@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}
-}