Teen Drug Sellers—An International Study of Segregated Drug Markets and Related Violence
Bibliographic record
Abstract
This study explores patterns of drug dealing in a multi-site sample of detained youth. Data are derived from the Drugs, Alcohol, Violence International (DAVI) study of male and female adolescents between the ages of 14–17 in four metropolitan areas: Amsterdam (The Netherlands), Montreal (Canada), Philadelphia (US), and Toronto (Canada). In a sample of 764 juvenile detainees, 60% overall reported predetention involvement in selling drugs, but this varied by site: 35% in Amsterdam, 61% in Philadelphia, 68% in Montreal, and 77% in Toronto. Typically, respondents were mostly selling drugs to friends and acquaintances. Cluster analysis revealed that teen drug sellers in our sample, despite the fact that many of them are involved in the sale of a variety of drugs, tend to specialize into three types of segregated markets: cannabis sellers, party drug sellers, and street drug sellers. Cannabis sellers are predominantly involved in selling marihuana and/or hashish, have relatively low transactions and sales, and violence is less common. Party drug sellers are distinguished by selling substances like ecstasy, powder cocaine, and amphetamines, and have high rates of violence. Street drug sellers' specialties are crack and heroin, and violence though common, is less prevalent than among the party drug sellers. These three types were found in all sites in our study, but were not equally prevalent across sites.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".