The Trans-Pacific Partnership agreement and public health: why we should be concerned.
Bibliographic record
Abstract
In October 2012, Canada became a negotiating member of the Trans-Pacific Partnership (TPP) agreement along with 11 other Pacific Rim countries. Widely touted as "a model for 21st-century trade agreements,"1 it extends well beyond traditional trade issues into domestic policy, creating a number of concerns about its implications for public health. These concerns include potential increases in pharmaceutical costs, the undermining of Canadian patent law, and strengthened investor rights over public health regulations to limit the consumption of products harmful to health. \n \nThe Comprehensive Economic and Trade Agreement (CETA) currently being negotiated between Canada and the European Union has already been forecast to increase Canadian drug costs by between $850 million and $1.6 billion annually by extending patent protection; leaked text of the TPP suggests that its provisions would increase these costs further.2 (See also Box 1.) The TPP's draft chapter on intellectual property rights goes beyond CETA, allowing the patenting of new forms and uses of old drugs regardless of efficacy, and introducing the patenting of diagnostic, therapeutic, and surgical methods. Although the rationale for extending patents is that it will lead to increased research and development spending in Canada, brand-name pharmaceutical companies have failed to comply with similar commitments in the past
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.047 | 0.043 |
| Insufficient payload (model declined to judge) | 0.007 | 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".