'Terrorist' or 'Mentally Ill': Motivated Biases Rooted in Partisanship Shape Attributions about Violent Actors
Bibliographic record
Abstract
We investigated whether motivated reasoning rooted in partisanship affects the attributions individuals make about violent attackers’ underlying motives and group memberships. Study 1 demonstrated that on the day of the Brexit referendum pro–leavers (vs. pro–remainers) attributed an exculpatory (i.e., mental health) versus condemnatory (i.e., terrorism) motive to the killing of a pro-remain politician. Study 2 demonstrated that pro– (vs. anti–) immigration perceivers in Germany ascribed a mental health (vs. terrorism) motive to a suicide attack by a Syrian refugee, predicting lower endorsement of punitiveness against his group (i.e., refugees) as a whole. Study 3 experimentally manipulated target motives, showing that Americans distanced a politically-motivated (vs. mentally ill) violent individual from their ingroup and assigned him harsher punishment— patterns most pronounced amongst high group identifiers.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".