Intuitive anger in the context of crime and punishment
Bibliographic record
Abstract
Anger is a key emotion in terms of understanding public responses toward crime: it is frequently mobilized in public discourses and is elicited by specific incidents. This study focuses on intuitive anger: a rapidly emerging negative emotional response that eschews principles of punishment but nevertheless contributes to punitiveness. This study uses facial electromyography to measure intuitive anger, and assess its effect on punitiveness drawing on data collected among university students in Canada (N = 87). The study’s repeated-measures experimental design allows for testing the hypotheses that: (i) individuals will experience greater intuitive angry responses when making punitive decisions for purported ‘stereotypical criminals’ and that (ii) anger will appear early in the decision-making process, and lead to more punitive decisions. The results of crossed-multilevel regression models provide evidence of the manifestation of anger within half of a second of individuals’ engagement in punitive decision-making. Intuitive anger follows principles that are central to intergroup relations, although it was not found to be predictive of rapid punitive decisions. The findings are discussed in terms of the moral and social implications of intuitive anger in the context of punitiveness toward crime.
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 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 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".