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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".