Diversifying Without Discriminating: Complying with the Mandates of the TRIPS Agreement (with R. Dreyfuss)
Bibliographic record
Abstract
Although the technological community was once fairly united in its needs from the patent system, the recent debate over patent reform has made it clear that this is no longer the case. Rather, it has become increasingly difficult to believe that a one–size–fits–all approach to patent law can survive. In this brief contribution to a symposium tackling Diversity in Innovation Policy, we consider the ways in which intellectual property obligations, most notably the TRIPS Agreement, circumscribe the ability of national lawmakers to tailor patent protection to reflect the concerns of different industries. In particular, we propose that TRIPS art. 27, which is cast in terms of nondiscrimination, should be interpreted to permit “differential treatment.” First, we argue that in other areas, treating different cases differently is not always invidious discrimination. Second, we note that many of the proposals for tailoring are not aimed at the nominal legal rights created by patent law, but rather at the economic effects of these patents, a distinction of significance in the WTO’s Canada-Pharmaceutical Patents case. Finally, we suggest that member states claiming de facto discrimination should be required to demonstrate some element over and above those required to establish de iure discrimination, and that member states defending an exclusion should be permitted to rebut a showing of disparate treatment by demonstrating a legitimate purpose. While decision makers will need to evaluate the relation between the stated purpose and the means chosen, this analysis would permit members to adopt most of the tailoring initiatives discussed during the Symposium. We give weight to the normative claims of the TRIPS Agreement to facilitate and enhance free trade. But we think that industry–specific patent laws are fully consistent with the language and purpose of the TRIPS Agreement as well as the comparative advantage philosophy that undergirds the modern trade regime.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".