What Food is to be Kept Safe and for Whom? Food-Safety Governance in an Unsafe Food System
Bibliographic record
Abstract
This paper argues that discussion of new food-safety governance should be framed by the realization that the dominant food system within which food-safety governance is designed to makes food safe is itself a structural and systemic sources of food un-safety, poor health and a future of food insecurity for many. For some, an appropriate policy response lies in addressing the connections between the food system and diseases such as heart disease, obesity and diabetes. For others it means subsuming food-safety governance within food security governance. For yet others, safe food implies food sovereignty governance and the primacy of a climate change resilient food system. Conventional approaches to food-safety governance are typically framed within a liability model of responsibility that has limited usefulness for addressing institutional, structural or systemic sources of harm such as those critics increasingly attribute to the dominant food system and which are not amenable to remedy by food-safety governance as it is widely understood. One cannot identify critical hazard points where risk is to be managed. These are food-system safety challenges. Because food-safety governance is so deeply political there needs to be greater attention to issues of governance rather than the more usual focus on the technologies of food-safety. Feminist political theorists have much to contribute to re-thinking food-safety governance in the context of diversity and the complexities of power. One could usefully start with the simple questions, “what food is to be kept-safe, for whom and who is the subject of food-safety governance in a post-Westphalian political economic order?” These questions can help unpack both the narrow parochialism and the misleading universalism of food-safety talk. This paper answers that neither the citizens of a particular state (or network of states) nor the falsely universalizing identity of ‘the consumer’ are adequate answers to these questions about ‘who’ and ‘what’. Answering these questions about who and what with respect to food-safety governance brings issues of justice, ecology, public health and the legitimacy and nature of governance itself into the heart of food-safety discussions.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".