Body counts : medical quantification in historical and sociological perspective = Body counts : La quantification medicale, perspectives historiques et sociologiques
Bibliographic record
Abstract
In an invigorating comparative and interdisciplinary reconsideration of the role of different types of medical 'counting', this wide-ranging bilingual volume takes us from the mortality tables of the eighteenth century to the movement for 'evidence-based medicine' in our own day. Culled from the proceedings of 'La quantification dans les sciences medicales et de la sante: perspective historique' held at the Musee Claude-Bernard in France in 2002, Body Counts moves beyond the usual emphasis on public health and clinical medicine to include the central role of numbers in laboratory work and medical instrumentation.Body Counts provides an innovative, historical, and sociological account of the functions of quantification. Contributors include Luc Berlivet (INSERM, CNRS, Paris), Alberto Cambrosio (McGill University), Sir Iain Chalmers (James Lind Library, Oxford), Nicholas Dodier (INSERM, CNRS, Paris), Michael Donnelly (Bard College), Volker Hess (Humboldt-University), Peter Keating (University of Quebec at Montreal), Ann La Berge (Virginia Tech University), Ilana Lowy (INSERM, CNRS, Paris), Harry M.Marks (Johns Hopkins University), Lion Murard (INSERM, CNRS, Paris), Mark Parascandola (National Cancer Institute, Bethesda, Maryland), Theodore M. Porter (University of California at Los Angeles), Andrea Rusnock (University of Rhode Island), Christiane Sinding (INSERM, CNRS, Paris), and Ulrich Trohler (Institut fur Geschichte der Medizin der Albert-Ludwigs-Universitat).
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".