Is It Who You Know, or How Many That Counts? Criminal Networks and Cost Avoidance in a Sample of Young Offenders
Bibliographic record
Abstract
The aim of the current study is to assess whether criminal networks can help young offenders avoid contacts with the criminal justice system. We examine the association between criminal network and cost avoidance specifically for the crime of cannabis cultivation in a rural region in Quebec, Canada. A self‐report delinquency survey, administered to the region's quasi‐population of high‐school students (N = 1,166), revealed that a total of 175 adolescents had participated in the cannabis cultivation industry (a 15% lifetime prevalence rate). Forty‐seven respondents (27%), including 29 who were arrested, reported having participated in a cultivation site that was detected by the police. Results indicate that “who you know” matters in the cultivation industry, and is an important independent predictor of arrest: very few young growers who were embedded in adult networks were apprehended. Conversely, embeddedness in a youth network emerged as an independent risk factor, especially embeddedness in larger networks.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".