The Politics of Digital Agricultural Technologies: A Preliminary Review
Bibliographic record
Abstract
Abstract Digital technologies are being developed and adopted across the agro‐food system, from farm to fork. Within decision‐making spaces, however, little attention is being paid to political factors arising from such technological developments. This review draws from critical social sciences to examine emerging technologies and big data systems in agriculture and assesses some key issues arising in the field. We begin with an introduction and review of the so‐called ‘digital revolution’ and then briefly outline how political economy is effective for understanding major challenges for governing technologies and data systems in agriculture. These challenges include: (1) data ownership and control, (2) the production of technologies and data development, and (3) data security. We then use literature and examples to consider the extent to which the political and economic landscape can be shifted to support greater equity in agriculture, while reflecting on structural challenges and limits. In doing so, we emphasise that while there are significant systemic tensions between digital ag‐tech development and agroecological approaches, we do not see them as mutually exclusive per se. This article intends to provide decision‐makers, practitioners and scholars from a wide range of disciplines with a timely assessment of agro‐food digitalisation that attends to political economic factors. In doing so, this article contributes to policy and decision‐making discussions, which, from our perspective, continue to be rather technocentric in nature while paying little attention to how digital technologies can support agroecological systems specifically.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".