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Record W2283784854

The Recognition of Indigenous Peoples’ and Community Traditional Knowledge in International Law

2001· article· en· W2283784854 on OpenAlexaff
Rosemary J. Coombe

Bibliographic record

VenueSSRN Electronic Journal · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicInternational Maritime Law Issues
Canadian institutionsYork University
Fundersnot available
KeywordsIndigenousLawPolitical scienceInternational lawTraditional knowledge
DOInot available

Abstract

fetched live from OpenAlex

Today I want to explore some of the international law-making efforts with respect to indigenous and traditional environmental knowledge. My work over the past three years has involved the study of the ongoing efforts underway to implement state obligations under the Convention on Biological Diversity ("CBD"), and the related efforts of the World Intellectual Property Organization (WIPO), to recognize, protect, and compensate for the contributions of indigenous and traditional communities' knowledge, innovations, and practices to the preservation and maintenance of biological diversity. This is a fascinating process of international lawmaking and an increasingly important field of global politics, which may or may not result in the establishment of new intellectual property rights. Perhaps more significantly, these efforts have served to expose the shortcomings and inadequacies of existing regimes of intellectual property, contributing to a crisis of legitimacy in the world intellectual property system. WIPO has recognized this to a degree, by acknowledging that the organization faces new criticism, questions, and inquiries because in the process of developing new international standards, many nations and groups felt excluded. Thus, according to statements made by the Deputy Director General, Geoffrey Sau Kuk Yu, WIPO has recognized that it must reach out to "new beneficiaries" and "move downstream into civil society" to engage users of intellectual properties as well as creators of new kinds. Only if groups feel they are a part of the dialogue and they have some stake in the system, he acknowledged, will they support it rather than undermine it.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0170.076
Scholarly communication0.0130.014
Open science0.0020.010
Research integrity0.0060.011
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations43
Published2001
Admission routes1
Has abstractyes

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