Medicinal Marijuana Production Creates Problem Residential Properties: A Routine Activity Theory Explanation and a Situational Crime-Prevention Solution
Bibliographic record
Abstract
Objectives: Illicit production of marijuana on residential properties creates significant health and safety problems. Health Canada grants licences to individuals to produce medicinal marijuana for personal use, conditional on their compliance with all appropriate regulations. Health Canada does not inspect licensees’ activities to monitor regulatory compliance, and privacy legislation prevents Health Canada from sharing licence holders’ details with third parties. This research examines how effective this administrative structure is at preventing medicinal marijuana from being produced in residential buildings by licence holders. Methods: The indoor production of marijuana requires substantial amounts of electricity. From 2005, addresses in Surrey, British Columbia, with exceptionally high power consumption have been provided to the municipal government for the purposes of undertaking fire safety inspections. This paper examines the outcome of inspections at 1,204 marijuana-production sites (n = 252 medicinal, n = 952 illicit) to see whether the licensing process prevents marijuana production in residential buildings. The illicit-production sites inspected by the city are used as a non-random comparison group for the medicinal sites. Findings: This inspection process has identified an increasing number of medicinal- (relative to illicit-) production sites in recent years. Medicinal-production operations were significantly less likely to be located in residential buildings. However, the medicinal residential sites that were detected were located in equivalent parts of the city to the illicit residential operations. Residential medicinal-production sites presented fewer electrical and biological safety problems relative to illicit-production sites, but all residential medicinal-production sites breached zoning and legislative requirements relating to land use, building safety, and structural integrity. Conclusions: The current administrative structure for licensing medicinal marijuana production does not prevent residential buildings from being used as marijuana-production sites. Routine activity theory is used as a platform to explain how additional situational prevention mechanisms can be used to prevent licensed medicinal marijuana production from creating building-related health and safety problems in the future.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".