Ownership of Open Data: Governance Options for Agriculture and Nutrition
Bibliographic record
Abstract
Ownership rights are a major factor in access and use of open data, distinct from yet as important as the availability of education, skills, technology, infrastructure, and finances. There are real deficits in law, understanding, and frameworks for governing open data ownership. These challenges must be addressed to achieve meaningful and equitable open data as default. The chief policy lesson from this paper is that moving to a model where data is open as default requires change in legal, social and technological norms, which all influence ownership of agriculture and nutrition data. Copyrights are not the only, nor even most important, legal rights establishing ownership of data. Relevant legal rights that facilitate access to and use of data at the international, national and subnational level include copyrights, database rights, technical protection measures, trade secrets, and patents and plant breeders’ rights, privacy and even tangible property rights. The open data community must broaden its engagement in all these areas to address emerging challenges.
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How this classification was reachedexpand
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 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".