A call to expand disciplinary boundaries so that social scientific imagination and practice are central to quests for ‘responsible’ digital agri‐food innovation
Bibliographic record
Abstract
Abstract This editorial introduces a special issue (SI) concerning quests for responsible digital agri‐food innovation. We present our interpretations of the concepts of responsible innovation and digital agri‐food innovation and show why they can and have been productively interrelated with social science theories and methods. First, each of the articles in this SI is briefly introduced and synthesised around three themes: (1) the need for a critique of digital ‘solutionism’ in current interdisciplinary research, development and innovation settings; (2) that social science contributes value via the ideas it brings to life to challenge dominant power dynamics and (3) that social scientific imagination and practice is a valuable long‐term investment to both mitigate risk but also embrace socioenvironmental opportunities as we face ongoing sustainability crises into the future. Second, we identify future research considerations arising within the field, sitting at the intersection of social science and agricultural sociotechnical transitions. Our insights relate to challenges and opportunities to ‘do’ social science within the context of contemporary and nascent transitions such as increasing digitalisation. Researchers trained in social science theory and practice can make distinctive contributions to agri‐food innovation processes by making social stakes visible and by advancing inclusive processes of research policy and technology design.
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 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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.011 | 0.023 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".