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Record W51546868 · doi:10.18584/iipj.2012.3.4.1

Revisiting the Debate on Intellectual Property Rights and Traditional Knowledge of Biodiversity: Accommodating Local Realities and Perspectives

2012· article· en· W51546868 on OpenAlexvenueno aff
Ujjal Kumar Sarma, Indrani Barpujari

Bibliographic record

VenueInternational Indigenous Policy Journal · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsTraditional knowledgeIndigenousIntellectual propertyTribePoliticsLaw and economicsPosition (finance)Political scienceIndigenous rightsSociologyEnvironmental ethicsLawBusinessEcology

Abstract

fetched live from OpenAlex

The international and national debates and developments on the applicability of an intellectual property rights regime for protecting traditional knowledge associated with biodiversity is over a decade old. Nevertheless, this continues to be an area fraught with difficulties for many reasons, such as inherent mismatch between the nature of intellectual property rights regimes and that of traditional knowledge, lack of an effective international framework, and alleged lack of will on the part of developed countries. The paper argues that the possible non-inclusion of traditional knowledge holders in the process and the lack of their practical capacity is another key reason for non-effectiveness of existing or envisaged legal instruments. It takes the position that a major lacuna of this discourse is that it is not strongly positioned in the local economic, political, and social contexts in which local and Indigenous communities find themselves today. Using a field-based case study of an Indigenous scheduled tribe, the Karbis in the northeastern state of Assam, the paper makes the case for discarding commonly held, often non-realistic ‘assumptions’ about local and Indigenous communities and accommodation of their realities and perspectives in enacting ‘rights based’ law and policy on these issues.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.405
Threshold uncertainty score0.661

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.051
GPT teacher head0.254
Teacher spread0.204 · 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 designObservational
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

Citations12
Published2012
Admission routes1
Has abstractyes

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