International Trade, Intellectual Property Rights And Traditional Knowledge: The Case of Plant Genetic Resources
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The research, development and commercialization focus of genetically modified (GM) crops is undergoing a shift from production-trait characteristicssuch as herbicide tolerance and insect resistance -to output-trait characteristics -such as nutraceuticals/functional foods and plant-made pharmaceuticals.It can be expected that, unlike the former focus, the latter focus will increasingly rely upon traditional knowledge to identify plants with characteristics beneficial to human health.These plants will then be subject to the techniques and procedures of modern biotechnology in order to isolate and extract those characteristics for the development of products (by mostly multinational corporations) that are protected by intellectual property rights (IPRs) and likely to be extensively traded across national boundaries.To proponents, this represents bioprospecting: a critical component of the innovative process of bringing human health benefits to all and not just those fortunate enough to benefit from the traditional knowledge because they live in a particular geographic or cultural zone.Yet, to critics, this represents biopiracy: a disingenuous repackaging of traditional knowledge in order to secure monopoly rents for the biopirate while excluding the original innovator from a claim to these rents.The objective of this paper is to examine the bioprospecting -biopiracy debate in the context of traditional knowledge as an important component in an aboriginal economic development strategy.It is concluded that in order to maximize the economic development 75
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it