Moralizing partisanship when surrounded by copartisans versus in mixed company
Bibliographic record
Abstract
) express these partisan moralization views. Critically, we compare the rates of partisan moralization not only when users are in contexts (subreddits) of their ingroup (e.g. r/democrats, r/vegetarian, r/Conservative, r/Hunting) but also when in mixed-company contexts populated mostly by users without partisan engagement (e.g. r/Music, r/Parenting). First, we developed four word embedding models-two for the users of each political side, one based on their comments in their ingroup contexts and one based on their comments in mixed-company contexts. Then, we evaluated the words of each model on two semantic dimensions, partisanship and morality, and we examined their correlation as an indicator of the expressed partisan moralization. Our first analysis demonstrated that LW users express moralized partisanship to a similar degree when surrounded by copartisans and when in mixed company. However, the moralized partisanship expressed by RW users in mixed company is weaker than that they express among copartisans, as well as that expressed by LW users in mixed company. In a second analysis, we divided partisan contexts based on whether they are inherently political (e.g. r/democrats) or not (e.g. r/vegetarian). This second analysis revealed that RW users express moralized partisanship more strongly than LW users in inherently political contexts, but right- and left-wingers are similar in nonpolitical partisan contexts. The discussion considers potential explanations for these asymmetries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".