Bibliographic record
Abstract
This dissertation examines whether changes in immigration are associated with changes in crime rates at the macro-level over time in Canada. Specifically, I analyze this relationship in Canadian census metropolitan areas (CMAs) and provinces for the period 1976-2011. In general, the research on the relationship between immigration and crime has shown that they are either negatively associated or not related at all. However, most of this work has been conducted in the United States using cross-sectional designs and has focused on one type of crime, namely homicide. Differences between Canada and the United States in the extent and nature of both immigration and crime warrant a study of their relationship and its generalizability beyond the US. My dissertation adds to the literature by using a longitudinal design â which treats immigration as a process that unfolds over time â and extending the analysis beyond homicide to include violent, property, and crime rates. My findings show that, controlling for demographic and socioeconomic covariates, changes in immigration are either not significantly associated or negatively associated with changes in crime rates. These results lend support to the generalizability of the findings from studies of US cities to Canadian cities, to larger units of aggregation (i.e., provinces),and across different types of 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".