Decentralisation of the Compliance of Anti-tobacco Law in India: The Case of Higher Educational Institutions in New Delhi, India
Bibliographic record
Abstract
Introduction This paper studies the decentralised compliance responsibility of India's tobacco control legislation, called the Cigarettes and Other Tobacco Products (Prohibition of Advertisement and Regulation of Trade and Commerce, Production, Supply and Distribution) Act of 2003, with its rules, which the government has outsourced to higher educational institutions, studied through an example of New Delhi. The study looks into the three most important parameters of decentralised compliance. Two of these require the installation of signboards by higher educational institutions, and the third involves imposing and collecting fines against persons found smoking within the educational institutions. Regarding the boards, the first board is about the warning prohibiting the sale within 100 yards of educational institutions, and the second one prohibits smoking in educational institutions. The study also checks with the educational institutions whether there is a presence of cigarette and tobacco product vendors within 100 yards of the institution, where the sale of such products has been banned by law. The study also found educational activities to create awareness in the institutions for tobacco control and cessation. Methods Thirty-six higher educational institutions, including universities, were randomly selected and studied through a unique methodology using India's transparency law, i.e., the Right to Information Act 2005. The portions of the law, which was direct compliance, or related compliance by the higher educational institution was included in the study. This justified the decentralised responsibility of these higher educational institutions. Applications for information under the transparency law were requested and supplied. Out of the 47 institutions, in which information requests were filed, 36 provided the information under the law. Credible information was provided by the higher educational institutions and this information was collated and interpreted to provide insights into the compliance by the higher educational institutions. Results Only 44.4% of the institutions had a board prohibiting the sale of cigarettes and other tobacco products. This was a non-universal compliance by the higher educational institutions. 88.9 per cent had boards prohibiting smoking in the higher educational institutions. Only one out of the 36 had an instance of smoking recorded and collection of fines. While 47.2% reported to not have any instance of smoking and fine collection. Fifty per cent of the institutions had no record of fine collection amounting to defiance of the law. 75 per cent of institutions did some kind of awareness activities which was not a direct statutory requirement, but a recommended guideline. Conclusion The results show that the intent to decentralise the compliance of the tobacco control law in New Delhi by the higher educational institutions has not been universally successful and requires much effort for its on-ground penetration. Such studies have a policy impact as they serve as an example for the generalisability of such statutes, which only work when there is implementation from the bottom-up and when the deterrent is also strong with incentivisation to the educational institutions to implement tobacco control with vigour.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".