The Role of Intuitive Anger in Public Punitiveness: An Investigation Into the Influence of Anger on People’s Reactions to Crime
Bibliographic record
Abstract
Anger is a fundamental human emotion characterized by opposition, impatience, and dissatisfaction. It is usually triggered during particular events and could result in a variety of physiological and behavioral reactions (DeCelles et al., 2020). This study focused on the role of anger in people’s reactions to crime. Specifically, it investigated intuitive anger, a quick and automatic negative emotional response that opposes principles of punishment but still contributes to punitiveness. To explore the influence of intuitive anger on the tendency to impose punishment or penalties on others, this study used facial electromyography (fEMG) and collected data from students at McGill University in Canada (N= 40). The present study’s repeated-measures experimental design would enable testing of the hypothesis that when making punitive decisions for alleged «stereotypical criminals,» individuals will exhibit greater responses of intuitive anger. It was anticipated that the display of images depicting stereotypical criminals would result in a substantial rise in instinctive anger response compared to images of atypical criminals. This is because the former are often perceived as lacking warmth (meaning, a lack of friendliness, kindness, and approachability) (Fiske et al., 2002). Therefore, this approach suggests potential variations in emotional responses based on the type of image presented.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".