Shifting boundaries of public health : Europe in the twentieth century
Bibliographic record
Abstract
In contrast to histories of twentieth century public health that focus exclusively on the local, national, or international levels, 'Shifting Boundaries' explores the connections or 'zones of contact' between the three levels. The interpretive essays, written by distinguished historians of public health and medicine, focus on four topics: the oscillation between governmental and non-governmental (public) agencies as sites of responsibility for addressing public health problems; the harmonization of nation states' agendas with those of international agencies; the development by public health experts of knowledge that is both placeless and respectful of place; and the transportability of model solutions across borders. The volume breaks new ground in its treatment of public health as a political endeavor by highlighting strategies to prevent or alleviate disease as a matter not simply of medical techniques, but of political values and commitments. Contributors: Peter Baldwin, Iris Borowy, James A. Gillespie, Graham Mooney, Lion Murard, Dorothy Porter, Sabine Schleiermacher, Susan Gross Solomon, Paul Weindling, and Patrick Zylberman. Susan Gross Solomon is professor of political science at the University of Toronto. Lion Murard and Patrick Zylberman are both senior researchers at CERMES (Centre de Recherche Médecine, Sciences, Santé et Société), CNRS-EHESS-INSERM, Paris
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.010 | 0.041 |
| Scholarly communication | 0.021 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".