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Record W3191302691 · doi:10.54119/jflp.evtd3096

Toward a Constructive Engagement: Agricultural Biotechnology as a Public Health Incentive in Less-developed Countries

2011· article· en· W3191302691 on OpenAlexaff
Chidi Oguamanam

Bibliographic record

VenueJournal of Food Law & Policy · 2011
Typearticle
Languageen
FieldMedicine
TopicBiotechnology and Related Fields
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsConstructiveIncentiveAgricultural biotechnologyAgricultureBusinessPublic healthBiotechnologyPublic economicsEconomicsComputer scienceBiologyMicroeconomicsMedicine

Abstract

fetched live from OpenAlex

Discourses on global public health crises, especially as they impact the less-developed world, focus mostly on the issue of access to life-saving drugs for needy populations. Also, they implicate the misalignment of global pharmaceutical research and development (R&D) agenda with the health needs of the poor. Equally attracting significant attention is the role of intellectual property in driving up the cost of drugs and exacerbating the drug access freeze to needy populations. More often, the conceptual strings of these discussions are woven around a complex interaction of themes, including those of globalization, the development narrative, and strategic changes in international lawmaking, especially in the areas of intellectual property, international trade, and the correlating supervisory international institutional and global governance regimes.

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)0.080
metaresearch head score (Gemma)0.041
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 score0.080
Threshold uncertainty score0.422

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0800.041
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0220.078
Scholarly communication0.0340.017
Open science0.0030.030
Research integrity0.0290.021
Insufficient payload (model declined to judge)0.0040.000

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 head0.084
GPT teacher head0.305
Teacher spread0.221 · 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

Citations0
Published2011
Admission routes1
Has abstractyes

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