Intellectual Property and the Right to Adequate Food: A Critical African Perspective
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.049 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".