A “Criminal Immigrant” Mindset and Punitiveness: The Canadian Case
Bibliographic record
Abstract
Unnever and Cullen (2010) argue that there is a “culturally universal” relationship between racial/ethnic/immigrant animus and general punitiveness. Because this thesis seems ill-fitting to Canada’s multicultural society, we re-examine the connection in Canada between punitiveness and intolerance associated with new immigrants. We do this by expanding their multivariate analyses of the Canadian case to consider additional data sources spanning the first decade of this century, and by testing directly their thesis that the relationship is mediated by citizens imputing criminal activity to negatively-viewed outgroups. We show that the relationship between immigrant intolerance and punitiveness reported in their original research for the year 2000 remains strong in 2004, 2008 and 2011 and resists explanation in terms of potentially relevant third variables. Our supplementary study examining the capacity of a criminal immigrant mindset variable to mediate this association shows that mediation is partial only. We conclude (1) that outgroup animus and general punitiveness are indeed related in the Canadian case, (2) that there is modest support for the Unnever/Cullen account of that relationship, but (3) that most of the original relationship remains unexplained.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".