Food, bodies, health (risks): the biopolitics of organic materiality testing in the context of diet-associated health risk management practices
Bibliographic record
Abstract
This article analyses the apparatus of practices dedicated to reducing long-term diet-associated health risks so as to question how risk is framed and worked upon in everyday risk governance contexts. In 2020–2021, I conducted 10 semi-structured interviews with public & environmental health researchers and programme managers at both federal (Canada) and provincial (Quebec) levels, as well as with clinical practitioners (clinical physician, dietician). I also used these interviews and the information provided by my interviewees to gather a corpus of materials comprised of clinical protocols, empirical guidelines, governmental screening programmes, and more. In the analysis, I contrast testing practices dedicated to preventing and controlling chronic conditions associated with food ingestion such as type 2 diabetes, with others dedicated to predicting and preventing health conditions associated with the ingestion of pesticides and contaminants. I use Foucauldian discourse analysis methods as well as Sheila Jasanoff’s (1999) “songlines of risk” in her analysis of environmental risk assessment practices to analyse how the risk mitigation practices under study integrate different approaches to risk and thus different ways of caring for health and bodies. This leads me to a discussion on the biopolitical approach to risk and health that informs these practices and the apparatus they constitute, contributing to orienting where the onus of the responsibility lies when it comes to managing or preventing diet-associated health conditions. I argue that the apparatus plays a role in invisibilizing the environmental factors in disease causation and reinforcing the individualisation of health (responsibility) rather than its collectivisation.
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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 | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".