The Interurban Network of Criminal Collaboration in Canada
Bibliographic record
Abstract
The interurban network of criminal collaboration in Canada is described, and possible explanations for its structure are explored. The data include all police-reported co-offences in the 32 major cities of Canada during 2006–09. Component analysis and graph drawings in network space and in geospace elucidate the structure of the network. Quadratic assignment procedure multiple regressions, repeated separately on the networks of instrumental and noninstrumental co-offences, test hypotheses about possible determinants of the network structure. The cities form one connected component, containing two clusters connected by a link between Toronto and Vancouver. One cluster, centred on the triad of Toronto, Montreal, and Ottawa, comprises the cities in Ontario and Quebec, with weak links to cities in the Atlantic provinces. The other cluster, centred on Vancouver, comprises the cities in the four western provinces. The structure is strongly correlated with the residential mobility of the general population, which in turn is strongly correlated with intercity distances. The correlation with mobility is less strong for instrumental than for noninstrumental crimes. The structure of this co-offending network can be explained by criminals’ routine activities, namely ordinary residential mobility, but the alternative explanation of purposive interurban criminal collaboration is more plausible for instrumental 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".