A study of pharmaceutical data exclusivity laws in Latin America: is access to affordable medicine threatened?
Bibliographic record
Abstract
During the 1994 Uruguay round of the General Agreement on Tariffs and Trade (GATT), the member states negotiated the Trade Related Aspects of Intellectual Property Agreement (TRIPS). TRIPS, which is administered by the World Trade Organisation (WTO), guides and elaborates the minimum standards for worldwide intellectual property regulation. However, over the past several years, countries with the most extensive intellectual property right protection schemes have pursued an increasing number of bilateral free trade agreements (FTAs) in different parts of the world. The FTAs that have been (and are currently being) negotiated are accused of including provisions that go beyond the common requirements of the TRIPS Agreement, and that trespass into a domain commonly known as TRIPS-plus, where it is thought the provisions might be unjust or too onerous for some countries. The debate is especially topical when it comes to the field of pharmaceutical technology, and whether provisions are likely to prejudice public health by making access to medicines substantially more difficult.However, few studies have attempted to catalog and empirically analyze countries' laws to discover how intellectual property rights have evolved – and advanced – since the TRIPS Agreement was signed. The present study attempts to do exactly this, while focusing on the particular case of data exclusivity laws for pharmaceuticals to determine if in this field of technology, TRIPS-plus protection of intellectual property is coalescing and developing into any sort of global or regional norms.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".