Patterns of Youth Participation in Cannabis Cultivation
Bibliographic record
Abstract
The current study examines the patterns of youth participation in cannabis cultivation by developing a typology among a sample of young offenders (n=175) in a rural region of Quebec, Canada known for its extensive outdoor cultivation industry. A hierarchical cluster analysis approach is used to group participants on various dimensions: motivation, substance use, delinquency and type of participation in cannabis cultivation. We also explore the role that criminal networks have in structuring the nature of youth involvement in the cultivation industry. Two general categories of participants emerged: participants for which cultivation is mainly a money generating activity (Entrepreneurs and Generalists), and participants who grow for personal use and intangible rewards (Hobbyists). Further, we found another group, the “helpers”, who qualify as “participants” to the cultivation industry, but not as “growers” per se. For generalists, participation to the cultivation industry is found among a portfolio of other crimes, while entrepreneurs tend to specialize in cultivation and are rewarded by achieving a higher level of success. Our results also suggest a correlation between the intensity of involvement in cultivation and the size of a youth's criminal network.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".