Os efeitos do NAFTA na destruição do México para justificar políticas comerciais transnacionais e alterações na cultura alimentar
Bibliographic record
Abstract
A obra Eating NAFTA: Trade, Food Policies and the Destruction of Mexico (2018), de Alyshia Gálvez, publicada em inglês pela editora da Universidade da Califórnia, e ainda sem tradução no Brasil, esclarece detalhadamente e de maneira oportuna a relação entre a perda da biodiversidade, a migração, as alterações na cultura alimentar mexicana, as doenças relacionadas à dieta e os acordos comerciais internacionais. Com foco no Acordo de Livre Comércio da América do Norte (Nafta), em vigor desde 1994, analisa as duas décadas seguintes ao acordo evidenciando seu impacto na vida dos mexicanos que vivem nos dois lados da fronteira. Para além da obra resenhada, este texto apresenta os últimos desdobramentos que levaram às implicações e alteração do NAFTA transformando-o em Acordo Estados Unidos-México-Canadá (USMCA, na sigla em inglês para United States-Mexico-Canada Agreement).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".