Trade Policy and Health: Adding Retrospective Studies to the Research Agenda Comment on "The Trans-Pacific Partnership: Is It Everything We Feared for Health?"
Bibliographic record
Abstract
Prospective studies of the potential health consequences of trade and investment treaties, such as the Trans-Pacific Partnership, are critical. These studies can make visible to trade policy-makers the potential negative impacts associated to such treaties and can influence the outcomes of such negotiations. However, few researchers have examined retrospectively the consequences of trade agreements. With more than 400 trade agreements and more than 2000 investment treaties currently in force, researchers have a large corpus of agreements to analyse in order to assess not only their potential impacts on health system and population health, but also their actual impacts. This comment suggests some research questions that would benefit from retrospective inquiry.
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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 | 0.049 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.057 | 0.051 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".