Bibliographic record
Abstract
Contemporary global order for the promotion of innovation exaggerates the role of intellectual property (IP) as a closed proprietary model of knowledge production and protection. Partly as a boomerang effect of that order or partly as a coincidence of the phenomenal rise in the information and communication technologies or both, there has been increased gravitation toward open, collaborative, shared, communal and interdependent models of innovation. This trend is typified by the rise of open software movement and cognate endeavours. The article attempts to transpose the open innovation dynamic to the context of plant genetic resources for food and agriculture (PGFA); and draws attention to the customary seed sharing and exchange as the centre-piece of the inherent open nature of innovation in agriculture, especially in indigenous and local communities. Focusing on the emergent institutional and legal frameworks for the governance of PGRFA, the article finds that they reflect pragmatic attempts at melding both the IP-driven closed model and the accommodation of open or public goods approach toward the promotion of access and overall management of innovation in PGRFA. It concludes that IP is not necessarily antithetical to open innovation, but could be calibrated to advance it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".