Who grows cannabis at home, and why?
Bibliographic record
Abstract
A new report (Hough et al. 2003) examines the domestic cultivation of cannabis in England and Wales and outlines reasons—ranging from the medicinal to the social to the commercial—why people take it up. The researchers found a variety of growing techniques, from under-the-stairs cultivation with strong lights, to a few pots on the windowsill using natural fertilizers and soil mixtures, to hydroponics favoured by the small commercial grower. The implications for policing the different groups were considered, and a wide variation in charging and cautioning practices was found. Home Office statistics do not distinguish between production and cultivation offences: both are recorded as production. There were 1960 cannabis production offences in the United Kingdom during 2000. Of these offenders, just under a quarter (458) received a police caution and the remainder (1502) were dealt with in court; just under a fifth (243) received a custodial sentence. The study examines a variety of possible legislative approaches with the aim of assessing the implications of the planned change to the laws on cannabis in the UK. It concludes that ‘if the government were to bring the treatment of cultivation for personal use into line with the amended law on possession, there would be no breach of the limits imposed by the UN drug conventions’. The report was commissioned by the Joseph Rowntree Foundation, an independent social policy research and development charity based in York.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".