Intellectual property rights trump the right to health: Canada’s Access to Medicines Regime and TRIPs flexibilities in the context of Bolivia’s quest for vaccines
Bibliographic record
Abstract
The failure of the Canadian pharmaceutical company Biolyse Pharma to obtain authorization under Canada’s Access to Medicines Regime (CAMR) to produce 15 million badly needed doses of a generic copy of a vaccine needed by a developing country is the occasion for a reflection on the right to health and the compatibility of this right with the dominant system of intellectual property rights (IPR) under the 1994 Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS), the Doha Declaration and subsequent decisions. Global health justice and intellectual property rights are difficult to reconcile because patent-supported pricing limits equitable access to medicines and, in addition, produces distortions in the allocation of resources in health research. A ‘delinkage’ of production and development costs is therefore called for. In spite of this, governments of countries with an important pharmaceutical sector are often hesitant to agree to sharing of information and technologies. The ‘flexibilities’ (mainly ‘waivers’ and ‘compulsory licenses’) provided for under the TRIPS system are not applied in a serious and consistent way in order to allow developing countries to deal with health crises, as is illustrated by extreme variation in rates of vaccination between the developed and the less developed world.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.054 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".