Bibliographic record
Abstract
Medical nutritional therapy (MNT) is a key feature of treatment for and management of type 2 diabetes. There are two elements to this therapeutic approach: collecting a diet history and prescribing therapeutic diets. For the clinical encounters observed in this paper, MNT often became a source of conflict between practitioners and patients. As clinicians endeavor to collect accurate information regarding food choices and eating so as to offer appropriate medical advice, and patients struggle to come to terms with a sickness in which food and eating have become toxic and risky, "judgments of taste" regarding food and patterns of eating become especially profound for both practitioners and those who seek treatment. Bourdieu's (1984) insights into the social situatedness of "taste" provides a useful framework for examining clinical practice and individual foodways. MNT is based upon and promotes a "taste for necessity," - "a form of adaptation to and consequently acceptance of the necessary" (ibid. 372), which judges food choices and eating patterns in terms of the bio-function of food and eating. In addition to this particular judgment of taste, study participants managing type 2 diabetes rely on other "judgments" that have been cultivated over the course of their own socially situated lives. At a time of disease these judgments of taste are conjoined as ongoing, multiply inflected lived histories of food and eating. Collecting life histories of food is one useful method for researching these patterned and idiosyncratic food and eating experiences.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".