Understanding the social implications of digital agricultural technologies
Bibliographic record
Abstract
The current digital agricultural revolution presents significant possibilities, promising transformative changes in agri-food systems. While advocates foresee enhanced efficiency, profitability, and sustainability, social movements and social critical scholars have concerns about its potential to perpetuate existing inequalities in the food system. The current conversation on the social implications of digital technologies often lacks a balanced perspective, either too broad and generic in scope or too narrowly focused on specific technologies. This imbalanced approach makes it difficult to inform meaningful policy debates or guide stakeholders who want to harness digital technologies to create more equitable and inclusive food systems. This paper contributes theory-based applied research to this discussion. We offer applied scholars and practitioners a Socio-Ethical Awareness Framework for Digital Agriculture, which recognizes the non-neutrality of technology, the central role of power, and the importance of data governance. The framework advocates for analyzing digital technologies based on the services they provide to farmers, while prompting questions about access, technology governance, and power distribution. Focusing on these aspects of digital technology can help ensure that these innovations support, rather than marginalize, small and limited-resource farmers.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".