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Record W1278386691

Genetic Resources and Traditional Knowledge: Case Studies and Conflicting Interests

2012· book· en· W1278386691 on OpenAlexaboutno aff
Tania Bubela, E. Richard Gold

Bibliographic record

Venuenot available
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsnot available
Fundersnot available
KeywordsTraditional knowledgeIntellectual propertyGenetic resourcesIndigenousEnvironmental ethicsCommonsPolitical sciencePoliticsIndigenous rightsNormativeSociologyGeographyAnthropologyLawEcologyPhilosophyBiology
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.344
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.264
GPT teacher head0.281
Teacher spread0.017 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations12
Published2012
Admission routes1
Has abstractyes

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