Emotions about crime and attitudes to punishment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Various polls and surveys seem to indicate that a substantial proportion of the Canadian public desires harsher penalties for crime. While various explanations have been offered for this punitiveness, emotional reactions to crime have been under-researched. The present research draws on a Canadian data set to test the hypothesis that the emotions of fear and particularly anger about crime are significant predictors of punitive attitudes once crime victimization, economic insecurity, internal attributions of crime causation and other variables are controlled for. This research also examines the possible indirect effects of economic insecurity, victimization and internal attributions of crime causation on punitiveness through their impact on fear and anger. The multiple regression results support the role of emotions, particularly anger, in explaining punitive attitudes. While indirect effects of victimization and economic insecurity were insignificant, 14 per cent of the effect of internal attributions was through anger.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it