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Record W2296161941

Intellectual Property and the Right to Adequate Food: A Critical African Perspective

2015· article· en· W2296161941 on OpenAlexaff
Chidi Oguamanam

Bibliographic record

VenueSSRN Electronic Journal · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIntellectual Property and Patents
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRight to foodIntellectual propertyFood securityHuman rightsFood sovereigntyCultural rightsPovertyLand grabbingFood systemsBusinessDevelopment economicsPolitical scienceAgricultureFundamental rightsInternational tradeEconomic growthEconomicsLawGeography
DOInot available

Abstract

fetched live from OpenAlex

Less developed countries, especially those in Africa, are buffeted by a complex combination of factors in their bid to realize the right to adequate food pursuant to the International Covenant on Economic, Social and Cultural Rights. Integral to that right are the ideas of freedom from hunger, poverty eradication, food security and food sovereignty. A number of factors assailing the realization of the right to adequate food in Africa include extreme weather conditions, via climate change dynamics; dysfunctional governments, political corruption, infrastructural deficits, and gaps in food and agricultural policies. Less obvious factors with potential to undermine the right to food include intellectual property and free trade; transformations in agricultural innovations and production, such as genetic modifications, monoculture and globalization of large scale industrial agriculture, intensification of mining and extractive industrial activities and, lately, the phenomenon of land grab. This article revisits the context for the introduction of IP in agriculture and the interplay of these enumerated factors and maps them onto the work of UN Committee on Economic, Social and Cultural Rights in its elaboration of the right to adequate food. It argues that even in the perceived negative impacts on the right to food of international IP and trade law obligations of African states, they still have the leverage to insist upon and to develop context-sensitive agricultural policies in the service of human right to adequate food by drawing inspiration from other developing countries that have maintained the primacy of the right to health over unfavorable patent laws.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.660
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.067
GPT teacher head0.243
Teacher spread0.176 · 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 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
Published2015
Admission routes1
Has abstractyes

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