Partisan Language in a Polarized World: In-Group Language Provides Reputational Benefits to Speakers while Polarizing Audiences
Bibliographic record
Abstract
We examine the impact of partisan language (language used to support a political agenda), both with regard to peoples’ perceptions of the speakers who use it and their evaluations of events it is used to describe. Two experiments recruited 1,121 Democrats and Republicans from the United States. Using a set of liberal-biased (expand voting rights) and conservative-biased (reduce election security) terms, we find that partisans judge speakers describing polarizing events using ideologically-congruent language as more trustworthy than those describing events in a non-partisan way (expand mail-in voting). However, when presented to rival partisans, ideologically-biased language promoted negative evaluations of opposing partisans, with speakers attributed out-group language being viewed as especially untrustworthy. Furthermore, presenting Democrats and Republicans with ideologically-congruent descriptions of political events polarized their attitudes towards the events described. Overall, the present investigation reveals how partisan language, while praised by co-partisans, can damage trust and amplify disagreement across political divides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 teacher head, 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".