Intellectual property sharing agreements in gene technology: implications for research and commercialisation
Bibliographic record
Abstract
In the early 1980s, countries began to allow patenting of biotechnological processes and products, creating technology advancements and rapid development of private industry. Part of the industry development that ensued was a consolidation of small firms and the creation of a few, large life science companies, each owning the requisite intellectual property (IP) and having freedom-to-operate. Despite the ability and potential gains from doing so, for many years there was very little apparent flow of IP between firms, separating potentially complementary technologies. A recent development in the ag-biotech industry, has been the increase in gene trait cross-licensing agreements. While these agreements hold much promise as means to facilitate the much needed sharing of IP, they raise additional concerns with respect to market concentration. This article examines publicly accessible information about the nature of these IP sharing agreements and the incentives they may create.
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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 | 0.024 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.021 | 0.042 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".