Contested definitions of digital agri-food system transformation: A webpage and network analysis
Bibliographic record
Abstract
This article explores how digital agri-food system transformations are framed and by whom. To answer these questions, we searched for webpages linked to Twitter and by Google that describe the role of emerging digital technologies in agri-food systems. From these, we characterize three framings of transformation. The first framing proposes that digital tools make farms optimally productive. A second framing emphasizes inequities in access to digital tools and increased farmer participation in tech development. A third framing highlights how technology creates more traceable agri-food systems. We then conducted a social network analysis of webpage authors, finding three network clusters. The largest centres on intergovernmental and international development organizations that typically promote the first and third framings. The second framing is mostly promoted by academic and civil society actors and was least common across webpages, suggesting that digital agriculture trajectories may overlook farmer autonomy and agency. Framings vary in the degree of transformation they promote and their consideration of smaller-scale farms’ needs. We suggest that digital agri-food system transformation efforts are more diverse than typically described in the literature. We recommend public and private actors work with academics and civil society organizations to enhance farmer inclusion in designing novel transformative approaches.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".