Bibliographic record
Abstract
List of Illustrations Acknowledgments List of Contributors 1 Plagues and Epidemics in Anthropological Perspective D. Ann Herring, McMaster University, Canada, and Alan C. Swedlund, University ofMassachusetts, Amherst 2 Ecosyndemics: Global Warming and the Coming Plaguesof the Twenty-first Century Merrill Singer, University of Connecticut 3 Pressing Plagues: On the Mediated Communicability ofEpidemics Charles L. Briggs, University ofCalifornia, Berkeley 4 On Creating Epidemics, Plagues, and Other WartimeAlarums and Excursions: Enumerating versus EstimatingCivilian Mortality in Iraq James Trostle, Trinity College, Connecticut 5 Avian Influenza and the Third Epidemiological Transition Ron Barrett, Macalester College 6 Deconstructing an Epidemic: Cholera in Gibraltar Lawrence A. Sawchuk, University of Toronto, Scarborough, Canada 7 The End of a Plague? Tuberculosis in New Zealand Judith Littleton, University ofAuckland, Julie Park, University of Auckland, and Linda Bryder, University of Auckland 8 Epidemics and Time: Influenza and Tuberculosis duringand after the 1918-1919 Pandemic Andrew Noymer, University of California, Irvine, and International Institute for Applied Systems Analysis, Austria 9 Everyday Mortality in the Time of Plague: OrdinaryPeople in Massachusetts before and during the 1918Influenza Epidemic Alan C. Swedlund, University of Massachusetts, Amherst 10 The Coming Plague of Avian Influenza D. Ann Herring and Stacy Lockerbie, McMaster University, Canada 11 Past into Present: History and the Making of Knowledgeabout HIV/AIDS and Aboriginal People Mary-Ellen Kelm, Simon Fraser University, Canada 12 Accounting for Epidemics: Mathematical Modeling andAnthropology Steven M. Goodreau, University of Washington 13 Social Inequalities and Dengue Transmission in LatinAmerica Arachu Castro, Harvard University, Yasmin Khawja, Yeshiva University, USA, and James Johnston, University of British Columbia, Canada 14 From Plague, an Epidemic Comes: Recounting Disease asContamination and Configuration Warwick Anderson, University of Sydney 15 Making Plagues Visible: Yellow Fever, Hookworm, andChagas' Disease, 1900-1950 Ilana Lowy, CNRS Paris 16 Metaphors of Malaria Eradication in Cold War Mexico Marcos Cueto, Universidad Peruana Cayetano Heredia 17 Steady with Custom: Mediating HIV Prevention in theTrobriand Islands, Papua New Guinea Katherine Lepani, Australian National University 18 Explaining Kuru: Three Ways to Think about an Epidemic Shirley Lindenbaum, City University of New York References Index
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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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.243 | 0.056 |
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".