Agricultural data governance from the ground up: Exploring data justice with agri-food movements
Bibliographic record
Abstract
Farmers and agri-food movements are responding to rapidly changing trends related to digitalization and datafication in agriculture. However, there is a lack of consensus on the potential of common ‘best practices’ to resolve agricultural data governance challenges and achieve data justice. To explore these complex dynamics, we present analysis from 40 workshops, conferences, and community dialogue events related to digital agricultural technologies and data governance between 2020 and 2023, involving the participation of farmers, farming organizations, government policy and programs staff, civil society, and academic researchers. We use a data justice lens to reorient the treatment of data governance challenges and approaches. We apply multiple dimensions of justice to examine the power relations and capabilities of diverse agri-food system actors to navigate the changing landscape of agricultural datafication. We find that many common practices in agricultural data governance have fundamental limitations to achieving data justice. Overcoming these limitations will require structural change, including new laws and regulatory frameworks, novel governance structures, capacity building, and solidarity across movements.
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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 | 0.078 | 0.159 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.014 | 0.032 |
| Scholarly communication | 0.020 | 0.032 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".