Cannabis cultivation and detection: A comparative study of Belgium, Finland and Denmark
Bibliographic record
Abstract
Research on cannabis cultivation has identified several factors associated with a grower's likelihood of detection by law enforcement. However, these studies are difficult to compare, as they drew from different data sources and methods, and have focused on only one geographical location. This article revisits the issue of detection using a large sample of cannabis cultivators recruited in three countries: Belgium (n = 659), Denmark (n = 560) and Finland (n = 1296). Respondents were recruited in the context of a self-reported online survey conducted successively in each country between 2006 and 2008. Multivariate analyses suggest several country-specific similarities and differences. Importantly, the Finnish growers reported being arrested significantly more often than Belgians or Danes. The probability that Finnish growers would be arrested increased with time spent on growing, the size of the cultivation site and when respondents did not work alone. In Denmark, the risks increased with the size of the c...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".