Immigration and Crime in Comparative Perspective: An Emerging Framework for Research
Bibliographic record
Abstract
Research on immigration and crime has experienced unprecedented growth. Studies reveal that immigration is not associated with increased crime rates in many countries including the United States, Canada, and Australia. In other places such as Europe, the findings are more mixed. Yet, limitations in this body of work hamper our understanding. In particular, researchers rely too heavily on conceptual dichotomies, or mutually exclusive categorizations (e.g., foreign-born vs. native-born, documented vs. undocumented, first generation vs. second generation), which insufficiently capture nuance or layers of diversity inherent in immigrant populations. Dichotomies must be replaced with an analytical framework that incorporates multiple dimensions of immigration. Beyond foreign-born (vs. native-born) status, intersections of immigrants’ legal statuses, assimilation levels, motives for migration, and settlement contexts create diverse groups whose backgrounds, experiences, and opportunities all have potential consequences for crime.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.006 | 0.048 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".