International Intellectual Property Agreements as Agents of Sustainable Development of Developing Countries
Bibliographic record
Abstract
The paper examines the implication of International Intellectual Property (ip) laws and agreements on the sustainable development of Least Developed Countries (ldcs) and Developed Countries (dcs) and suggests approaches for improving the development and wellbeing of people in the developing world through national ip laws. The paper argues that generally international ip agreements may appear biased against developing countries and most dcs are reluctant to challenge the status quo and/or use the flexibilities of the international ip agreement to promote the wellbeing of their citizens. However, the article finds that ldcs and dcs could change this trend through the creative use of national ip laws and international agreements to promote the sustainable development of ldcs and dcs. The major instrument suggested for this shift in approach is the establishment of national ip administration institutions and the positive use of compulsory licences.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".