Contested agri-food futures: Introduction to the Special Issue
Bibliographic record
Abstract
Abstract Over recent decades, influential agri-food tech actors, institutions, policymakers and others have fostered dominant techno-optimistic, future visions of food and agriculture that are having profound material impacts in present agri-food worlds. Analyzing such realities has become paramount for scholars working across the fields of science and technology studies (STS) and critical agri-food studies, many of whom contribute to STSFAN—the Science and Technology Studies Food and Agriculture Network. This article introduces a Special Issue featuring the scholarship of STSFAN members, which cover a range of case studies and interdisciplinary and transdisciplinary engagements involving such contested agri-food futures. Their contributions are unique in that they emerged from the network’s specific modus operandi: a workshopping practice that supports the constructive, interdisciplinary dialogue necessary for critical research and rigorous analyses of science and technology in agri-food settings. This introduction offers an overview of STS and critical agri-food studies scholarship, including their historical entanglements in respective studies of food scandals, scientific regimes and technological determinism. We illustrate how interdisciplinary engagement across these fields has contributed to the emergent field of what we term agri-food technoscience scholarship, which the contributions of this Special Issue speak to. After a brief discussion of STS concepts, theories and methods shaping agri-food policy, technology design and manufacturing, we present the eleven Special Issue contributions in three thematic clusters: influential actors and their agri-food imaginaries; obfuscated (material) realities in agri-food technologies; and conflictual and constructive engagements in academia and agri-food. The introduction ends with a short reflection on future research trajectories in agri-food technoscience scholarship.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".