Marijuana Cultivation in British Columbia: Using spatial and social network analysis techniques to inform evidence-based policy and planning
Bibliographic record
Abstract
This dissertation provides evidence and direction for policy makers dealing with the issue of marijuana production in British Columbia (BC). This document provides a descriptive analysis of the "grow op" industry before moving into a spatial analysis of the effect of police tactical teams (green teams) on grow operations. The final chapters focus on the involvement of organized crime in the marijuana production industry and employ a social network analysis (SNA) framework to illustrate the involvement of different clusters of criminal associations. Using the case of Vietnamese drug p roduction as an example, SNA and geographic information systems (GIS) analyses techniques are combined to assess the spatial and social linkage patterns in statistical and visual terms. The descriptive analysis of police records shows that marijuana "grow ops" increased dramatically from 1997 through 2000, before levelling out by the end of the collection period in 2003. A significant increase in the number of suspects of Vietnamese origin was also noted. The police hypothesize that Vietnamese criminal organizations have effectively taken over the production of marijuana in certain jurisdictions and that they work with other criminal organizations (i.e. Hells Angels and Southeast Asian Groups) to distribute the drugs. The results suggest that those areas with specialized anti-grow (or "green") teams show a significant decrease in grow operations in their jurisdiction. Compared to the rate of increase in the period preceding green team implementation, the treatment jurisdict ions experienced an 82% decline in marijuana cultivation facil ities. Neighboring control areas experienced a 7% increase in grow operations post-treatment. The network analysis of drug production networks illustrates that the criminal networks involved in drug production are spatially constrained. It also shows that the distance between individuals in the drug production criminal network and their associates varies systematically with network characteristics (centrality measures) but not with demographics or criminal history variables. Of particular importance to police investigation into criminal organizations is the finding that central figures in the network, individuals high in betweenness, degree and closeness centrality, travel farther to associates and place themselves on the geographic periphery of the network habitat.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".