Interrogating Moral and Quantification Discourses in Nutritional Knowledge
Bibliographic record
Abstract
This conversation is part of a special issue on “Critical Nutrition” in which multiple authors weigh in on various themes related to the origins, character, and consequences of contemporary American nutrition discourses and practices, as well as how nutrition might be known and done differently. In this section authors focus on the hegemony of reductionism and quantification in modern-day nutritional knowledge by discussing the historical foundations and ethical dimensions, as well as the scientific absences, in this knowledge. Reviewing various challenges to the energy balance model, they all suggest that the promotion of good nutrition is far from simple. Some authors also discuss why various “invisible” nutrients and measures of good nutrition continue to hold so much sway in nutrition discourse.
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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 | 0.049 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.025 | 0.135 |
| Scholarly communication | 0.020 | 0.031 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.013 | 0.017 |
| 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".