Just Say No to the TPP: A Democratic Setback for American and Asian Public Health Comment on "The Trans-Pacific Partnership: Is It Everything We Feared for Health?"
Bibliographic record
Abstract
(TPP) policy and the severe threats to public health that it implies for 12 Pacific Rim populations from the Americas and Asia (Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore, United States, and Vietnam). With careful and analytic precision the authors convincingly unearth many aspects of this piece of legislation that undermine the public health achievements of most countries involved in the TTP. Our comments complement their policy analysis with the aim of providing a positive heuristic tool to assist in the understanding of the TPP, and other upcoming treaties like the even more encompassing Transatlantic Trade and Investment Partnership (TTIP), and in so doing motivate the public health community to oppose the implementation of the relevant provisions of the agreements. The aims of this commentary on the study of Labonté et al are to show that an understanding of the health effects of the TPP is incomplete without a political analysis of policy formation, and that realist methods can be useful to uncover the mechanisms underlying TPP's political and policy processes.
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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 | 0.035 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.018 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.064 | 0.072 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".