Political orientation and moral judgment of sexual misconduct
Bibliographic record
Abstract
<p xmlns="http://www.ncbi.nlm.nih.gov/JATS1">In a series of studies in the U.S. (total N participants <italic>=</italic> 4,828) using both news articles (Studies 1-2) and constructed scenarios (Studies 3-4), we investigated how judgments of responsibility, blame, causal contribution, and punishment for alleged perpetrators and victims of sexual misconduct are influenced by (1) the political orientation of media outlets, (2) participants’ political orientation, and (3) the alleged perpetrators’ political orientation. Results indicated that participants’ political orientation, and the interaction between participants’ and alleged perpetrators’ political orientation, predicted moral judgments. Conservative participants were generally more likely inculpate and punish alleged victims in all four studies. Both conservative and liberal participants judged politically-aligned alleged perpetrators more leniently than politically-opposed alleged perpetrators. This political ingroup effect was ubiquitous across all tests of the dependent measures for conservative participants; whereas it was muted and unreliable for liberal participants. The findings collectively demonstrate that moral judgments about sexual misconduct are politicized at multiple psychological levels, and in ways that asymmetrically affect victims.
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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 | 0.005 | 0.038 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".