From Denmark to Delhi: the multisectoral challenge of regulating<i>trans</i>fats in India
Bibliographic record
Abstract
OBJECTIVE: India has proposed legislating an upper limit of trans fat in partially hydrogenated vegetable oils and mandating trans fat labelling in an effort to reduce intakes. The objective of the present study was to examine the complexities of regulating trans fat in India by examining the policy processes involved and the perceived implementation challenges. DESIGN: Semi-structured interviews (n 18) were conducted with key informants from various sectors. Interviewees were asked about sources of trans fat in the food supply, existing policies that may influence trans fats and perceived challenges related to the proposed trans fat regulation, in addition to questions tailored to their area of expertise. Interview data were organised based on common themes. SETTING: Interviews were conducted in India. SUBJECTS: Interviewees were key informants from various sectors including agriculture, trade, industry and health. RESULTS: Several themes were identified related to the complexity of regulating trans fat in India. A lack of trans fat awareness, the large unorganised retail sector, a need for suitable alternative products that are both acceptable to consumers and affordable, and a need to build capacity were crucial factors affecting India's ability to successfully regulate trans fat. The limited number of food inspectors will create an additional challenge in terms of enforcement of trans fat regulation. CONCLUSIONS: Although India will face challenges in regulating trans fat, legislating an upper limit of trans fat in partially hydrogenated vegetable oils will likely be the most effective approach to reducing it in the food supply. Ongoing engagement with industry, agriculture, trade and processing sectors will prove essential in terms of product reformulation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".