Perceptions of Crime Seriousness and Punitive Attitudes in Post-Secondary Students
Bibliographic record
Abstract
Public perceptions of crime seriousness and attitudes towards the punishment of crime stem from the social norms and values that shape society and are informed by ways of knowing about crime. Located within a social constructionist paradigm, the purpose of this study was to examine the influence of post-secondary education, crime type and crime representation on perceptions of crime severity and punitive attitudes for different crime types. A sample of 971 students from the University of Winnipeg completed an online questionnaire measuring perceptions of crime severity for one-line crime descriptions as well as crime scenarios based on actual court data. Results show that both wrongfulness and harmfulness are strong predictors of perceived seriousness. As predicted, violent crimes ranked highest on measures of seriousness, wrongfulness, and harmfulness, and received the most severe sentencing recommendations. While the level of education completed had no significant difference on perceptions of crime severity, differences between fields of study showed significance. Comparisons between responses to the one-line crime descriptions and the crime scenarios revealed significantly stronger severity ratings for the scenarios than for the one-line descriptions although the ranking of crimes remained similar. Findings suggest that universal notions of wrongfulness and harmfulness exist that influence perceptions of seriousness and are resistant to change. Perceptions towards crimes are informed by a socially constructed reality of crime that shapes our knowledge of crime. Understanding the underlying factors that influence perceptions and attitudes towards crime may shed new light on the social approaches to dealing with crime and provides new insights into crime control practices and government crime policy. Finally, results also emphasize the importance of reflecting on the matter of crime representation in academic research.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.001 |
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".