<scp>M</scp>exico attempts to tackle obesity: the process, results, push backs and future challenges
Bibliographic record
Abstract
Mexico's obesity prevalence is one of the world's highest. In 2006, academics, and federal and state government agencies initiated efforts to design a national policy for obesity prevention. The Ministry of Health (MOH) established an expert panel to develop recommendations on beverage intake for a healthy life in 2008. Subsequently, the MOH, with support from academia, initiated the development of the National Agreement for Healthy Nutrition (ANSA). ANSA was signed by all relevant sectoral actors in 2010 and led to initiatives banning sodas and regulating unhealthy food in schools and the design of other yet to be implemented initiatives, such as a front-of-package labeling system. A main challenge of the ANSA has been the lack of harmonization between industry interests and public health objectives and effective accountability and monitoring mechanisms to assess implementation across government sectors. Bold strategies currently under consideration include taxation of sugar-sweetened beverages, improvement of norms for healthy food in schools, regulation of food and beverage marketing to children and implementation of a national front-of-pack labeling system. Strong civil society organizations have embraced the prevention of obesity as their goal and have used evidence from academia to position obesity prevention in the public debate and in the government agenda.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 0.006 |
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".