Power Contestations in the Use of Agri-food Data: Towards a Sustainability Governance Approach
Bibliographic record
Abstract
Law is intrinsically embedded in politics. Prevailing dynamics and norms can significantly impact new legal rules; hence, there is a need to interrogate the spectrum of engagements of any given subject or phenomenon with the law. In the context of global governance of food and agricultural data, this article examines how power manifests in the generation and use of agri-food data, how power could construct global rules on the use of agri-food data and how the global community should respond to this realisation. It highlights the politics of technology and data and examines how these drive inequalities and inequities among certain actors and groups, taking the ensuing intersectional dynamics into account. These insights make important contributions to the debate on the global governance of food and agricultural data by shedding light on the analytical framework that can be used to recognise the unequal political economy within which the global governance of agri-food data is negotiated. It offers justifications on why and how such an opportunity should be used to correct these imbalances and redistribute the benefits of agri-food data to all stakeholders.
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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 | 0.056 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.089 |
| Scholarly communication | 0.024 | 0.024 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.007 | 0.006 |
| 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".