Agro-Biodiversity and Food Security: Biotechnology and Traditional Agricultural Practices at the Periphery of International Intellectual Property Regime Complex
Bibliographic record
Abstract
According to regime theory, inequities in the Trade-Related Aspects of Intellectual Property Rights (TRIPS) Agreement has provided a catalyst for counter-regime dynamics against TRIPS and the greater international intellectual property system (IIPS). The post-TRIPS regime analyses of the IIPS focus on intellectual property’s relationship with a number of external regimes, especially health, rights, biodiversity, and indigenous knowledge. The subjects of agricultural biodiversity (agro-biodiversity) and food security are equally important sites for the regime debate, but are not specifically addressed. The neoliberal economic system undermines agro-biodiversity, traditional agricultural practices, and thus food security in indigenous and local communities, in favour of protecting intellectual property in agricultural biotechnology (agro-biotech). Agro-biodiversity, traditional agricultural practices, and food security are marginalized issues addressed in critiques of the IIPS, despite the centrality of intellectual property in addressing agro-biodiversity and global food insecurity. This article situates agro-biodiversity and food security within the political economics of agriculture to highlight the complex dynamics that account for their virtual absence in the regime discourse and to underscore the weakness or constraints of the narrow framework of the regime debate.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.030 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".