Understanding global patterns of domestic cannabis cultivation
Bibliographic record
Abstract
Purpose Unlike other plant‐based drugs, cannabis is increasingly grown within the country of consumption, requires minimal processing before consumption, and can be easily grown almost anywhere using indoor or outdoor cultivation techniques. Developments in agronomic technologies have led to global growth in domestic cultivation, both by cannabis users for self‐ and social‐supply, and by more commercially‐oriented growers. Cross‐national research is needed to better understand who is involved in domestic cultivation, the diversity in cultivation practices and motivations, and cultivators' interaction with the criminal justice system and cannabis control policies. Design/methodology/approach The article introduces the Global Cannabis Cultivation Research Consortium (GCCRC), describes its evolution and aims, and outlines the methodology of its ongoing cross‐national online survey of cannabis cultivation. Findings Despite differing national contexts, the GCCRC successfully developed a core questionnaire to be used in different countries. It accommodates varying research interests through the addition of optional survey sections. The benefits to forming an international consortium to conduct web‐based survey research include the sharing of expertise, recruitment efforts and problem‐solving. Research limitations/implications The article discusses the limitations of using non‐representative online sampling and the strategies used to increase validity. Originality/value The GCCRC is conducting the largest cross‐national study of domestic cannabis cultivation to date. The aim is not only to better understand patterns of cannabis cultivation and how they differ between countries but also to build upon online engagement methodology with hidden populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".