The TRIPS Agreement as a Coercive Threat: Estimating the Effects of Trade Ties on IPR Protection Regimes
Bibliographic record
Abstract
Negotiators from developed countries pushed hard for the inclusion of the TRIPS Agreement in the WTO set of agreements because it was viewed as a potentially effective method of coercing developing countries to strengthen their protection of intellectual property rights (IPR). We investigate whether the threat of cross-agreement retaliation, which could be authorized in disputes regarding the TRIPS Agreement, is effective in changing countries’ IPR protection regimes. The results from a panel empirical model suggest that both the TRIPS Agreement and the strength of trade ties with developed countries are important determinants of IPR protection regimes, but the vulnerability to potential trade losses through cross-agreement retaliation is not a uniformly significant determinant across geo-economic regions. These results extend beyond the TRIPS Agreement and highlight the potential ineffectiveness of the WTO’s retaliation mechanism as a coercive threat.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".