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Record W2069429191 · doi:10.1086/ahr.115.1.260

Sherry Fields . Pestilence and Headcolds: Encountering Illness in Colonial Mexico . New York : Columbia University Press . 2008 . xxi, 188. $60.00.

2010· article· en· W2069429191 on OpenAlexaff
Luz María Hernández-Sáenz

Bibliographic record

VenueThe American Historical Review · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicLatin American history and culture
Canadian institutionsWestern University
Fundersnot available
KeywordsColonialismMedicineCausationSyphilisPlague (disease)HistoryGerontologyFamily medicinePolitical scienceAncient historyLawHuman immunodeficiency virus (HIV)Archaeology

Abstract

fetched live from OpenAlex

This is a welcome addition to the still slim collection of works on health in pre-Pasteurian Mexico. Deviating from previous institutional and professional studies, Sherry Fields seeks to explore contemporary systems of thought “through the prism of the sick-room” (p. x). Thus, her main concern is to understand how contemporaries understood disease causation and prevention and how they fought illness. The first chapter deals with illnesses that affected the residents of Mexico in pre-Hispanic and colonial times. After an introduction on pre-Hispanic disease, the chapter is divided into three sections on epidemic illness brought by the conquerors; endemic illnesses like malaria, syphilis, and digestive disorders; and everyday ailments like scabies, swellings, and toothache. The second chapter concentrates on the medical marketplace and the various types of medical practitioners offering their services to the public. The section that deals with pre-Hispanic times, more substantial in this chapter, is followed by others on licensed and unlicensed practitioners, curanderos, the Catholic Church, and divine healers.

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)0.001
metaresearch head score (Gemma)0.001
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: Review
Teacher disagreement score0.118
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0420.005

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 head0.020
GPT teacher head0.218
Teacher spread0.198 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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