Spillover effects following recreationallegalization of marijuana in borderingregions. : Analysis of spillover effect from legislation of marijuana in Washington using synthetic control.
Bibliographic record
Abstract
Legalizing marijuana for recreational use has been a hot political topic in recent years. Different conclusions have been drawn from the literature on this subject, but one conclusion is that the tactic is an effective instrument in combating the black market. On the other side, it has also been demonstrated that it has a negative effect on neighbouring regions that still view marijuana as an illicit drug. This study examines the evidence of any causal link between the legalization of marijuana for recreational use and its consequences on neighbouring regions. The legalization of marijuana in Washington state in 2012 and spillover effects on drug-related crime rates in British Columbia served as the foundation for this study. With the help of nine Canadian provinces, a synthetic British Columbia has been created that attempts to simulate how crime rates may have developed had Washington not legalized marijuana. The legalization of marijuana has had both positive and negative spillover impacts on the neighbouring territory, according to empirical data. As a "gateway" substance, marijuana possession rates rose after the implementation of the policy. Results on the supply side show that because of increased competition and legal supply from the neighbouring region, marijuana suppliers are switching to other drugs. This essay also addresses other potential social effects of marijuana legalization, such as a decline in the prevalence of sexual assault and marijuana possession among young people. Based on the empirical data, the study offers improvements in aiding neighbouring regions who are considering the implementation of RML in creating preventative measures against illicit usage of marijuana.
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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 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".