Legalization of Recreational Cannabis: Is India Ready for it?
Bibliographic record
Abstract
Cannabis is one of the oldest psychoactive substances in India and worldwide. Many developed countries like Canada, Netherlands and few states of the USA have legalized the use of recreational cannabis. However, In India, the recreational use of cannabis and its various forms such as ganja, charas, hashish, and its combination is legally prohibited. There have been several discussions and public interest litigations in India regarding the legalization of recreational cannabis use and its benefits. With this background, this article addresses the various implications of legalizing recreational use of cannabis, a multibillion dollar market and its impact on mental health, physical health, social, cultural, economic, and legal aspects with the lessons learnt from other countries that have already legalized recreational cannabis use. It also discusses whether India is prepared for the legalization of recreational cannabis, given the current criminal justice and healthcare systems. The authors conclude that, India is perhaps not enough prepared to legalize cannabis for recreational use. India's existing criminal justice and healthcare systems are overburdened, finding it challenging to control medicinal use, which is often the first contact point for cannabis-related concerns.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".