Intellectual property rights in plant breeding and biotechnology: Assessing impact
Bibliographic record
Abstract
This paper undertakes a comparative institutional analysis of intellectual property rights (IPRs) in the agricultural plant breeding sector in the EU, the US, Canada as well as evolving regimes in developing countries. The policy issue that motivates this paper is the optimal scope of legal protection to be provided for new plant varieties, including those that may contain potentially patentable biotechnological inventions such as modified genetic sequences. Countries are choosing different combinations of two types of IPRs, plant breeder’s rights (PBRs) and patents in addition to trademarks and trade secrets, while there are also pressures, for example through WTO TRIPS Agreement and negotiations for a patent treaty towards harmonization. The paper illustrates how the fierce debate surrounding the granting of IPRs in this sector reflects not only the redistributive effects of such property rights but also their relationship with informal customs and norms concerning farmers ’ rights over their seed. From a policy perspective, a transaction-cost based analysis of these IPRs favours the European approach to the American one. The paper therefore contributes to further developing the application of the economics of property rights to IPRs, which is challenging due to the technological change that may be induced by such rights.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".