Update on Overleaf.
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@@ -704,7 +704,7 @@ Our example relies on the publicly available Civil Comments dataset \citep{cjada
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Each comment was labeled by up to ten manual annotators (although selected comments were labeled by even more annotators). Originally, the dataset represents \emph{toxicity} and \emph{disclosure} as proportions of annotators who labeled a comment as toxic or as disclosing aspects of personal identity including race and ethnicity.
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For our analysis, we converted these proportions into indicators of the majority view to transform both variables to a binary scale.
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Our MLA method works in this scenario, as shown in \ref{fig:real.data.example.app}
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Our MLA method works in this scenario, as shown in \ref{fig:real.data.example.app} below.
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\begin{figure}[htbp!]
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\centering
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