Patent term extension and test data protection obligations: identifying the gap in policy, research, and practice of implementing free trade agreements
Bibliographic record
Abstract
Much of the academic literature criticizes the inclusion of patent term extensions (PTE) and test data protection into the pharmaceutical provisions and/or intellectual property (IP) chapters of free trade agreements (FTAs), with many arguing that such provisions will increase the cost of pharmaceuticals for the implementing government. Such arguments are often backed by studies conducted prior to the conclusion of the relevant FTA. This is problematic for several reasons, most notably that the studies make assumptions that subsequently turn out not to be false and that the claims are not revisited and supported with empirical data following implementation. This article reviews the experience of two jurisdictions – Canada and Australia – in order to provide an analysis of legislative and judicial practices with a focus on implications and the cost of FTAs. The article examines how Canada and Australia have implemented their FTA obligations domestically and on the hereto ignored but important role of courts. One key finding is how courts in both countries are vigilant in narrowing the scope of obligations under FTAs to accommodate the need of the domestic market. The article ultimately concludes by calling on governments to conduct a detailed analysis of PTE and test data protection so as to better inform and prepare policymakers and, ultimately, improved FTA provisions and health outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.064 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.014 | 0.039 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".