Bibliographic record
Abstract
Abstract Do broad, universal intellectual property rights bring the benefits of innovation, creativity, technical know-how, and foreign investment to developing countries? Or do treaties that require developing countries to grant greater intellectual property protection actually stifle development and impede access to the knowledge and essential medicines that the world's poor so desperately need? The debate over such questions has raged for decades, among scholars and diplomats, lawmakers and policy makers, nongovernmental organizations and international agencies, IP industries and development policy analysts. The Development Agenda is the fruition of developing countries' most recent campaign to ensure that the intellectual property treaty regime permits—and, indeed, empowers—developing countries to tailor their intellectual property laws as they deem necessary to promote development and serve the welfare of their citizens. The Agenda's adoption by the World Intellectual Property Organization (WIPO) in September 2007 is an historic watershed for that UN agency, which has long viewed its mandate as the promotion of greater intellectual property rights throughout the world. This book examines the Development Agenda and the broader issues it raises. Our contributors include leading scholars from various disciplines, including economics, political science, and law, and from countries at various stages of development, including China, India, Brazil, Argentina, Chile, Nigeria, Egypt, and Israel, in addition to the US, Canada, and EU. They also include experts from NGO-think tanks, UNCTAD, and two Brazilian diplomats who stood at the forefront of advocating for the Development Agenda's adoption at WIPO.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.091 | 0.026 |
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".