The influence of offender and victim ethnicity on perceptions of crime severity and recommended punishment
Bibliographic record
Abstract
Crime severity has been found to be one of the best predictors of sentencing decisions (Darley, Carlsmith, & Robinson, 2000). There is however a dearth of research examining the effect of offender and victim ethnicity on perceptions of crime seriousness, and the few studies that do exist have produced equivocal findings. Some studies find an effect of victim ethnicity (e.g., Cohen-Raz, Bozna, & Glicksohn, 1997), some studies find no significant effects of offender nor victim ethnicity (e.g., Benjamin, 1989), and some studies only find effects under certain conditions, such as when the crime is of low seriousness (e.g., Herzog, 2003a). The present study was conducted in an attempt to clarify these convoluted findings by using measures of modern and old-fashioned prejudice. Whereas old-fashioned prejudice refers to the belief that an out group is in someway inferior, modern prejudice refers to the view that a minority group no longer faces discrimination or that the minority group is being “too pushy” when advocating for equal rights (McConahay, 1983). Using a sample of undergraduate psychology students, it was found that when the crime was perceived as being quite severe, harsher punishments were recommended for the offender. Further to this, participants scoring high in modern prejudice perceived crimes to be more severe and recommended longer sentences in certain offender-victim ethnicity conditions than participants scoring low in modern prejudice. However, contrary to the hypotheses, no significant differences were found between high and low old-fashioned prejudice participants. Perceived offender responsibility and stability were also found to affect perceptions of crime severity and recommended punishment. When an offence was described as being stable (i.e., the offender had committed similar crimes in the past), participants rated the crime as being more severe and recommended a harsher punishment than when it was the offender’s first offence. Additionally, when participants attributed responsibility for the crime to the offender, crime severity ratings were higher and recommended punishments were longer. The implications of these results are discussed and recommendations for future research are put forward.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".