Reconstituting populations through evidence-based medicine: an ethnographic account of recommending procedures for diagnosing type 2 diabetes in clinical practice guidelines
Bibliographic record
Abstract
This ethnographic investigation responds to calls for more social science research on -- versus for or against -- evidence-based medicine (EBM). It centers on the recommendations endorsed, in a set of clinical practice guidelines, by Canadian specialists in the late 1990s for diagnosing diabetes. Empirically, the article mainly relies on public presentations and discussion at the Canadian Diabetes Association Professional Conferences (1997-2001), supplemented by findings from documentary sources, direct observation, participant-observation and interviews. It confirms the importance of clinical reason and clinical epidemiology as preconditions for EBM, the textually mediated character of EBM and patients' bodies as sites for producing knowledge crucial to EBM. Most significantly, it also demonstrates the importance of non-patients' bodies as sites for producing knowledge that is crucial to EBM and its politics. EBM politics encompass the discursive, socio-technical and visceral reconstitution of populations.
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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 | 0.043 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.040 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| 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".