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Record W2594098120 · doi:10.18584/iipj.2017.8.1.3

Traditional Knowledge in the Time of Neo-Liberalism: Access and Benefit-Sharing Regimes in India and Bhutan

2017· article· en· W2594098120 on OpenAlexvenueno aff
Indrani Barpujari, Ujjal Kumar Sarma

Bibliographic record

VenueInternational Indigenous Policy Journal · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicConservation, Biodiversity, and Resource Management
Canadian institutionsnot available
Fundersnot available
KeywordsCommodityContext (archaeology)IndigenousTraditional knowledgePolitical scienceCitizen journalismEthosEconomic growthEconomicsPolitical economyLawGeographyEcology

Abstract

fetched live from OpenAlex

In a neoliberal world, traditional knowledge (TK) of biodiversity possessed by Indigenous and Local Communities (ILCs) in the global South has become a valuable "commodity" or "bio-resource," necessitating the setting up of harmonized ground rules (international and national) in the form of an access and benefit-sharing regime to facilitate its exchange in the world market. Despite criticisms that a regime with a neo-liberal orientation is antithetical to the normative ethos of ILCs, it could also offer a chance for developing countries and ILCs to generate revenue for socioeconomic development—to which they are gradually becoming open, but only under fair and equitable terms. Based on this context, this article proposes to look into the legal and policy frameworks and institutional regimes governing access and benefit sharing of TK associated with biological resources in two countries of South Asia: India and Bhutan. The article seeks to examine how such regimes are reconciling the imperatives of a neo-liberal economy with providing a just and equitable framework for ILCs and TK holders, which is truly participatory and not top-down.

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 score0.027
Threshold uncertainty score0.419

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 science0.0010.000
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 head0.038
GPT teacher head0.293
Teacher spread0.255 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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