Changes in apparent consumption of staple food in Mexico associated with the gradual implementation of the NAFTA
Bibliographic record
Abstract
In 1994, the United States, Canada, and Mexico signed the North American Free Trade Agreement (NAFTA) to remove trade barriers and facilitate cross-border trade in goods and services. Worldwide, trade agreements, urbanization and economic development have shaped significant changes in dietary habits. This study aims to evaluate the association between the gradual implementation of NAFTA and changes in apparent consumption of staple foods in Mexico. We analyzed national apparent consumption of animal- and vegetable-source foods, using data from the Food and Agriculture Organization of the United Nations (FAO) from 1970 to 2018. Association between NAFTA and apparent consumption was estimated using interrupted time series analysis (ITSA) with synthetic controls and included two inflection points based on the implementation of NAFTA: 1994, when the agreement began, and 2008 when it was fully implemented. As a result, comparing Mexico with the synthetic control, we found a significant decrease in apparent consumption of pulses, -3.22 and -1.92 kcal/capita/day in the post-1994 and post-2008 periods, respectively. The vegetable-source foods showed an increase of 5.79 kcal/capita/day after 2008. The trends of apparent consumption of animal-source foods, eggs, and milk had significant increases after 1994 and 2008. The apparent consumption of meat increased only after 2008. The implementation of NAFTA was associated with an increase in apparent consumption of food from animal-source and a decrease in consumption of pulses. After 2008, an increase in apparent consumption of vegetable-source foods was observed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".