Bibliographic record
Abstract
Research consistently finds that while the public expresses concerns about sentence leniency in the abstract, when presented with a specific case, people are typically not particularly punitive (Hough and Roberts 2012). While Canadian studies have further explored the effect of various social-structural factors on sentencing preferences, absent is any empirical investigation of the role, if any, that the offender's ethnicity plays. We explore this question using a convenience sample of adult Canadians and four vignettes (of an armed robbery), which were identical except for the racialized identity of the offender. Respondents' sentencing choices and perceptions of offender dangerousness, culpability, and recidivism risk were elicited. Results revealed that the “black” offender was rated as being significantly more dangerous than the “white” offender and also received a significantly more punitive sentence. After controlling for the impact of the criminal record and views of dangerousness, culpability, and recidivism risk, there was still an independent, albeit very small, effect of the racialized identity of the offender on sentencing preferences. The strongest predictor of the sentence, however, was how dangerous respondents viewed the offender. Part of the desire for a harsher sentence for the black offender likely related to views of dangerousness. The implications of these findings are discussed.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".