Bibliographic record
Abstract
The Canadian canola breeding sector met a transition from publicly funded breeding research to large private investments in research and development (R&D). The increasing use of biotechnology tools in the mid 1990s made the assignment of plant ownership technically possible while the legislative safeguards that were put in place during the same period enabled owners to take juristic actions against potential infringers. Today, canola breeding sector is dominated by large multinational firms. The generation of proprietary knowledge in the canola breeding sector has caused a freedom to operate issue. Private and public firms conducting canola R&D are seriously concerned about their ability to gain and preserve access to key technologies in an IPR world. This thesis uses the tragedy of the anticommons framework to analyze the consequences of increased intellectual property protection in the canola breeding sector. Theory suggests that when a common resource is owned by multiple owners, each of the owners has the incentive to overcharge potential users, leading to the underuse of the resource. In R&D, different owners of complementary technologies may overcharge potential R&D firms that want to assemble different technological pieces to produce a new one. The result is forgoing research and development of new products.The results of personal interviews with thirteen canola researchers and IP officers are presented and analyzed. The results suggest that the increase in the intellectual property protection in the last two decades in the canola breeding sector has led to difficulties with canola R&D. These difficulties take the form of reduced access to current, proprietary and public material. With hampered access to research input material, research output is not maximized and potential research may be forgone. Interviewees described how the increase in the intellectual property protection affects their personal and organization’s ability to conduct research as well as some the implications of the new IP regime on the canola breeding sector. There is indication that canola breeding sector is moving towards a super-protectionism. Under these conditions, canola R&D firms, private and public, are in search for ways that will open access to enabling technologies and research areas. The creation of platform technologies and collaborations are the most prominent ones and are observed to increase in occurrence world wide.
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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 | 0.001 | 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 | 0.013 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
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".