Social Opportunity Structures and the Escalation of Drug Market Offending
Bibliographic record
Abstract
Objectives: This study looks at whether social opportunity structures are associated with transitions into more serious drug market offending. Our focus is on the speed at which transitions occurred, and whether variations in criminal embeddedness play a role in explaining this. Methods: A survey of 520 North American cannabis cultivators allowed us to assess one dimension of the criminal career—escalation—looking at the speed of transitions from cannabis user to grower. Our main predictor, criminal embeddedness, was measured through the presence of a cultivation mentor involved in cannabis cultivation. Results: Cox proportional hazard regression analysis demonstrated late cannabis use onset and an indicator of the number of drugs used beyond cannabis were found to accelerate transitions. In addition, within-person changes in mentorship were found to influence the timing of escalation, with meeting a mentor associated with quicker transitions into cannabis cultivation. Conclusions: Findings emphasize the role of mentors as gateways into new milieus. Results support increased attention to the immediate social networks and broader social opportunity structures in which offenders and would-be offenders are embedded as major factors driving the timing of onset into more serious criminal pathways.
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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 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".