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Record W4412732134 · doi:10.64252/nmdxs355

“Cannabis Farming In Malana: A Socio-Legal Study Of Culture, Environment, And Law With A Comparative Perspective From Canada”

2025· article· en· W4412732134 on OpenAlexaboutno aff
Shefali Mahendru

Bibliographic record

VenueInternational Journal of Environmental Sciences · 2025
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsnot available
Fundersnot available
KeywordsPerspective (graphical)CannabisAgricultureLegal cultureLawPolitical scienceSociologyGeographyPsychologyArchaeologyArtPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.307
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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