“Cannabis Farming In Malana: A Socio-Legal Study Of Culture, Environment, And Law With A Comparative Perspective From Canada”
Bibliographic record
Abstract
Cannabis cultivation in the remote Himalayan village of Malana has long been integral to local culture and economy, yet it conflicts with India’s national narcotics laws. This paper provides a socio-legal analysis of Malana’s cannabis tradition, examining cultural norms, environmental impacts, and legal frameworks. It integrates case studies and official reports to describe how Malana’s unique self-governance and historic practices clash with the Narcotic Drugs and Psychotropic Substances (NDPS) Act (1985). The study also compares India’s approach with Canada’s legalized model, highlighting regulatory differences and outcomes. We review literature on Himalayan cannabis heritage, environmental studies of high-altitude cultivation, and cannabis policy. Key findings include that Malana’s harsh terrain leaves few viable crops besides cannabis and that eradication efforts drive illicit plantations into fragile forests, causing erosion. The Canadian experience shows that strict regulation and legal markets can reduce illicit trade, though challenges remain. Finally, we identify gaps in research on socio-environmental trade-offs and propose policy recommendations for India, such as permitting controlled hemp cultivation with local safeguards and investing in alternative livelihoods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.020 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".