A method to detect criminal organizations from police data
Bibliographic record
Abstract
Definitional problems in the area of organized crime have traditionally led to measurement problems that trickle down the criminal justice system. This study quantifies the broad conception of organized crime in the Canadian legal context and examines the types of crimes in which criminal organizations (and organized criminals) are involved. To estimate incidents potentially related to organized crime, we combine police-reported data from Montreal, Canada, with the three components of organized crime as prescribed by the Criminal Code of Canada: size, offence severity and continuity. The strategy of combining models on a continuum, varying in co-offending unit size, as well as offence severity provides both restrictive and inclusive estimates, accounting for the main discrepancy dividing scholarly and policy assessments of organized crime. Results showed that from 2005 to 2009, the extent and severity of incidents potentially related to organized crime that emerged from the three family of models proposed varied, ranging from 184 to 2086 incidents. The models also showed variations in incident rates across crime classification types with most organized crime incidents attributed to property and violent offences. This study is one of the first to propose a set of methods to detect incidents potentially related to organized crime using police data and to illustrate the potential implications of restrictive and inclusive measures for estimating its prevalence.
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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 | 0.023 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".