Bibliographic record
Abstract
We live in an age of evidence-based healthcare, where the concept of evidence has been avidly and often uncritically embraced as a symbol of legitimacy, truth, and justice. By letting the evidence dictate healthcare decision making from the bedside to the policy level, the normative claims that inform decision making appear to be negotiated fairly—without subjectivity, prejudice, or bias. Thus, the term ‘‘evidence-based’’ is typically read in the health sciences as the empirically adequate standard of reasonable practice and a means for increasing certainty. Supporters believe that evidence-based medicine (EBM) can introduce rational order to the deliberative processes of healthcare decision making. It is perhaps puzzling, then, to come across critical perspectives (typically arising from the humanities and the more theory-driven social sciences) raising concerns about a seeming technogovernance being introduced by this deferral to the evidence where power interests can be obfuscated by way of technical resolve. The critics holding this minority view argue that technological solutions to problems of knowledge and practice cannot replace medicine’s normative content. Against EBM’s democratic leanings toward transparency and accountability, medical criteria alone cannot decide valueladen ethically charged decisions. This paper attempts to explain and evaluate this important debate in the philosophy of medicine, focusing specifically on the dispute over 'evidence-based women's health'.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.015 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".