Genetic Resources and Traditional Knowledge: Case Studies and Conflicting Interests
Bibliographic record
Abstract
This fascinating study describes efforts to define and protect traditional knowledge and the associated issues of access to genetic resources, from the negotiation of the Convention on Biological Diversity to The Declaration on the Rights of Indigenous Peoples and the Nagoya Protocol. Drawing on the expertise of local specialists from around the globe, the chapters judiciously mix theory and empirical evidence to provide a deep and convincing understanding of traditional knowledge, innovation, access to genetic resources, and benefit sharing.Because traditional knowledge was understood in early negotiations to be subject to a property rights framework, these often became bogged down due to differing views on the rights involved. New models, developed around the notion of distributive justice and self-determination, are now gaining favor. This book suggests – through a discussion of theory and contemporary case studies from Brazil, India, Kenya and Canada – that a focus on distributive justice best advances the interests of indigenous peoples while also fostering scientific innovation in both developed and developing countries.Comprehensive as well as nuanced, Genetic Resources and Traditional Knowledge will be of great interest to scholars and students of law, political science, anthropology and geography. National and international policymakers and those interested in the environment, indigenous peoples’ rights and innovation will find the book an enlightening resource.
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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.023 | 0.018 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.010 | 0.006 |
| 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".