Sweet blood and social suffering: Rethinking cause-effect relationships in diabetes, distress, and duress
Bibliographic record
Abstract
I draw upon anthropological engagements with bioscience and embodiment in order to unpack current approaches to defining and preventing diabetes mellitus. The analysis stems from the conviction that carefully considering the symbolic frames through which we conceive of diseases, their origins, their distribution, and their consequences will assist us in planning and implementing interventions to improve population health. I argue that research and interventions focused on the sweetness of blood would benefit from rethinking intersections between diabetes, distress, and duress. In many instances, the lived experience of diabetes is consonant with an understanding of distress (i.e., "social suffering") that expands conventional understandings of population health problems. Diabetes incidence is rising worldwide, but it is rising especially rapidly in Aboriginal and other disadvantaged populations. Notably, diabetes is now three to five times more common in Canada's First Nations population than it is in its non-Aboriginal population. Yet as recently as 50 years ago, diabetes and associated health problems were rare in these groups. To come to grips with such transformations and disparities is to advance the population health research agenda.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".