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Record W574039417 · doi:10.4324/9781003086376

Plagues and Epidemics

2020· book· en· W574039417 on OpenAlexaboutno aff
D. Ann Herring, Alan C. Swedlund

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsHistoryGeographyVirologyMedicine

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.243
Threshold uncertainty score0.814

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2430.056

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.

Opus teacher head0.031
GPT teacher head0.284
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations18
Published2020
Admission routes1
Has abstractyes

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