Hollow Threats: Transnational Food and Beverage Companies’ Use of International Agreements to Fight Front-of-Pack Nutrition Labeling in Mexico and Beyond
Bibliographic record
Abstract
In October 2019, the Mexican government reformed its General Health Law thus establishing the warning approach to front-of-pack nutrition labeling (FOPNL), and in March 2020, modified its national standard, revamping its ineffective FOPNL, one preemptively developed by industry actors. Implementation is scheduled for later in 2020. However, the new regulation faces fierce opposition from transnational food and beverage companies (TFBCs), including Nestlé, Kellogg, Grupo Bimbo, Coca-Cola, PepsiCo through their trade associations, the National Manufacturers, American Bakers Associations, the Confederation of Industrial Chambers of Mexico and ConMéxico. Mexico, as a regional leader, could tip momentum in favor of FOPNL diffusion across Latin America. But the fate of the Mexican FOPNL and the region currently lies in this government's response to three threats of legal challenges by TFBCs, citing international laws and guidelines including the World Trade Organization (WTO), Codex Alimentarius, and the North American Free Trade Agreement (NAFTA)/US-Mexico-Canada Agreement (USMCA). In this perspective, we argue that these threats should not prevent Mexico or other countries from implementing evidence-informed policies, such as FOPNLs, that pursue legitimate public health objectives.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.007 |
| 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".