What's in the Blood? Temporalities at Play in Diet-Related Risk Management Testing Practices
Bibliographic record
Abstract
In this paper, I look at two different sets of practices that are part of the risk management apparatus in place in Québec & Canada to apprehend and control risks associated with food consumption. More specifically, I contrast diabetes and chemical contaminants risk management testing practices, so as to compare how both frame and approach risks, in a context where recent research in social sciences, epigenetics and environmental sciences increasingly points to "environmental" pathways of disease causation while many chronic conditions remain highly individualized in public and health discourses. The analysis pays close attention to the different temporalities discursively created, considered, and neglected in these practices in order to understand how risk is approached and worked on. This highlights the power relations that inform how we care (or not) for (certain) bodies, inflecting in particular ways their-uneven-becomings. I argue that the Canadian biotechnological apparatus of testing practices meant to apprehend and control diet-related risks contributes to foreclosing the temporalities of health and illness considered and acted upon. As such, the apparatus contributes to (re)producing inequalities, here mostly health related ones, as well as creating differentiated biological materialities.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.049 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".