Uneven effects of twenty years of Chile's cannabis policy implementation in cannabis onset
Bibliographic record
Abstract
BACKGROUND: In Chile, Laws 19366 and 20000, implemented in 1995 and 2005 respectively, regulated and sanctioned cannabis' personal use, cultivation and trafficking. METHODS: We use thirteen biannual cross-sectional national surveys data from 1994 to 2018 to examine the effect of Laws 19366 and 20000-using the rate of individuals incarcerated per 100000 population due to drug-related crimes as proxy-on the age of onset of cannabis use over time. We estimate the effect of these policies using a mixed proportional hazards framework that models the transition to first cannabis use in 47,832 individuals aged 12-21. RESULTS: Overall, changes in these laws did not affect the transition to first cannabis use. However, increases in the rate of individuals incarcerated were associated with decreases on the age of onset of cannabis use in females and individuals living in affluent neighborhoods or in specific regions. CONCLUSION: We find no evidence of cannabis policy changes affecting the age of onset of cannabis use across all individuals aged 12-21. Policy effects associated with decreases in cannabis onset age in females and individuals from affluent neighborhoods or specific regions can be explained by using theoretical frames that recognize specific dynamics of cannabis supply and demand.
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 | 0.003 | 0.009 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".