Impacts of Crime Attractors and Generators on Criminality in Montreal
Bibliographic record
Abstract
Urban institutions and facilities modulate criminal opportunities by influencing the number and type of people using them. Based on this finding, the present study attempts to assess the effect of several types of facilities, generally identified in the literature as attractors and generators of crime, on assault, theft, robbery, and motor vehicle theft in Montreal. To this end, the number of crimes and different facilities, as well as several socio-demographic indicators, were measured for the 3,195 dissemination areas. Except for laundromats and pawnshops, all the facilities assessed have a significant effect on one or more types of crime. The results obtained, which differ from the results for other North American cities, reject the generalization of such studies in other cities. While these differences could be attributed to dissimilarities at the urban or sociocultural level, it is also plausible that the methodological choices made can be the source of these differences. Limitations and future directions are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".