A Proposed "Agricultural Data Commons" in Support of Food Security
Bibliographic record
Abstract
This article identifies a data governance model that could help reduce dataset access inequities currently experienced by smallholder farmers in both developed-world and developing-world settings. Agricultural data is globally recognised for its importance in addressing food insecurity, with such data generated and used by a value chain of contributors, collectors, and users. Guided by the modified institutional analysis and development (IAD) framework, our study considered the features of agricultural data as a "knowledge commons" resource. The study also looked at existing data collection modalities practiced by John Deere, Plantwise and Abalobi, and at the open data distribution modalities available under the Creative Commons and the Open Data Commons licensing frameworks. The study found that an "agricultural data commons" model could give greater agency to the smallholder farmers who contribute data. A model open data licence could be used by data collectors, supported by a certification mark and a dedicated public interest organisation. These features could engender an agricultural data commons that would be advantageous to the three key stakeholders in agricultural data: data contributors, who need engagement, privacy, control, and benefit-sharing; small and medium-sized-enterprise (SME) data collectors, who need sophisticated legal tools and an ability to brand their participation in opening data; and data users, who need open access.
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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 | 0.042 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".