Bibliographic record
Abstract
This book is the product of a true collective effort to bring together people from a wide range of viewpoints to engage in a dialogue about trade and health.More than just representing countries from around the world, those who have participated represent wide-ranging perspectives on trade and health, their intersection, the extent to which they come into conflict, and what can be done about that conflict.Our profound thanks go to all who contributed their ideas, time, and energy, as well as to all the collaborating organizations and individuals involved in the preparation of this book.We would like to thank the following agencies who sponsored this project: the Canadian International Development Agency (cida), Health Canada, the International Development Research Centre (idrc), and the Department of Foreign Affairs and International Trade (dfait).The views and positions expressed in this book are the views of the authors and should not be seen as representing official policy of the organizations or governments that supported this project.Our thanks also go to the World Trade Organization (wto), the United Nations Conference on Trade and Development (unctad), and the Organization of American States (oas) for their contribution.We want to thank all of the sixty participants in the experts' workshop Trade and Health: Policy Coherence for Human Development, hosted by the Institute for Health and Social Policy at McGill University.Their presentations and inputs and the discussions that ensued were at the centre of this project.These contributions are now reflected in the book.We want to thank the Canadian Public Health Association for planning the logistics of the Montreal workshop.Federico Paredes and his colleagues at the Costa Rican Public Health Association (acosap)
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.341 | 0.263 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".