Does Use of Generic Depictions or Scenarios Matter in Perceptions of Crime Seriousness? An Empirical Test
Bibliographic record
Abstract
This study examined the influence of crime type and crime representation in survey design on perceptions of crime severity using one-line crime descriptions and crime vignettes. A unique feature of the study was the use of hate crime as an offence category. A sample of 917 university students completed an online questionnaire measuring perceptions of crime severity for one-line crime descriptions as well as crime scenarios based on actual court cases. Consistent with past research, the results showed that both perceptions of wrongfulness and harmfulness are strong predictors of perceived crime seriousness. Violent crimes ranked highest on measures of wrongfulness, harmfulness, and seriousness, while property crimes like break and enter tended to be ranked lowest in perceived severity. Hate crime was viewed as quite serious by respondents and was rated equivalent to high-scale fraud embezzlement in terms of severity. Comparisons between responses to one-line crime descriptions and crime scenarios revealed that scenarios elicited significantly stronger severity ratings, although hierarchical rankings for crime type remained similar. The representation of crime in academic research may affect participant responses. It is recommended that researchers consider the impact the choice of crime representation in survey tools has on measures of perceived crime severity.
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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 | 0.053 | 0.225 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".