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Record W3159762934 · doi:10.24908/iqurcp.7494

Intellectual Property Rights, Biotechnology and Discourses of Development: Internal Contradictions and Forms of Resistance

2017· article· en· W3159762934 on OpenAlexvenueno aff
Krishana Persaud

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsnot available
Fundersnot available
KeywordsIntellectual propertyModernization theoryPolitical scienceCovertCovenantPovertyPromotion (chess)Resistance (ecology)SociologySustainable developmentReductionismPolitical economyEnvironmental ethicsEconomic systemLaw and economicsSocial scienceLawEconomicsEpistemologyPoliticsBiology

Abstract

fetched live from OpenAlex

The International Covenant on Economic, Social and Cultural Rights identifies freedom from hunger and malnutrition as a fundamental human right of every individual. The current global food crisis undermines this right and has multi‐faceted repercussions for poverty reduction and sustainable development in the Global South. A plethora of explanations have been proposed regarding the causes of the current food crisis, while a biotechnological solution involving the expansion of Genetically Modified (GM) seeds in the Global South has gained renewed momentum and simultaneously increased resistance. This presentation will provide a nuanced understanding of the promotion of IPRs and biotechnological ‘inventions’ as contemporary facets of a hegemonic modernization discourse of development. By first critically examining the development of IPRs and their relation to biotechnology I provide a basis for understanding the internal contradictions of this technologically reductionist discourse. Using a detailed case study from India, I then illustrate the way in which the internal contradictions of this discourse result in particular forms of resistance which significantly challenge the structure of the global food system.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.077
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.085
GPT teacher head0.326
Teacher spread0.241 · 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.

Study designBench or experimental
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

Citations0
Published2017
Admission routes1
Has abstractyes

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