Drogue et crime : l’impact du commerce de drogues sur le tissu urbain
Bibliographic record
Abstract
The problem addressed in this analysis is whether « routine activities » of drug dependent criminals are associated with the spatial concentration of crime committed by these criminals. This problem is tested in a series of analyses including an investigation of the spatial pattern of the residential burglaries committed by drug dependent burglars using W.A.V. Clark's spatial choice housing search models. While Clark used the home and work place as nodes in the housing search, we use the home and drug market place as nodes in the criminal search of drug addicts. If the addict supports his or her habit with property crime, these nodes are expected to be a focal point for criminal activity in a distance minimizing scenario. The data indicate that the spatial concentration of property crime about drug market places means that a « crime containment » policy practiced by many police agencies is doomed to failure. Property criminals will continue to probe outward from a containment area which encompasses a drug market place. In fact, drug dependent property criminals may act as a vanguard for spatially expanding drug markets. Drug sellers and drug dependent property criminals seem to operate in a symbiotic relationship.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".