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Record W2106751054 · doi:10.1093/shm/hkt045

Appealing to the Republic of Letters: An Autopsy of Anti-venereal Trials in Eighteenth-century Mexico

2013· article· en· W2106751054 on OpenAlexfundno aff
Fiona Clark

Bibliographic record

VenueSocial History of Medicine · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies in Science
Canadian institutionsnot available
FundersArts and Humanities Research CouncilUniversity of AberdeenQueen's UniversityWellcome Trust
KeywordsIrishNarrativeHistoryMedicineArtLiteraturePhilosophy

Abstract

fetched live from OpenAlex

This study analyses the narrative elements of a little-known report into anti-venereal trials written by an Irish military physician-surgeon, Daniel O'Sullivan (1760–c.1797). It explores the way in which O'Sullivan as the narrator of the Historico-critical report creates medical heroes and anti-heroes as a means to criticise procedures initiated by staff in the Hospital General de San Andrés, Mexico City. The resulting work depicts a much less positive picture of medical trials and hospital authorities in this period than has been recorded to date, and provides a critical and complicated assessment of one of Spain's leading physicians of the nineteenth century, Francisco Javier Balmis (1753–1819).

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.989
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.010
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.112
GPT teacher head0.300
Teacher spread0.189 · 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.

Study designQualitative
Domainnot available
GenreEmpirical

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

Citations3
Published2013
Admission routes1
Has abstractyes

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