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Record W2309544395 · doi:10.1093/shm/hkv111

Anne Hardy,<i>Salmonella Infections, Networks of Knowledge, and Public Health in Britain, 1880–1975</i>

2016· article· en· W2309544395 on OpenAlexaff
James Hanley

Bibliographic record

VenueSocial History of Medicine · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsGerm theory of diseaseRelevance (law)Public healthSociology of scientific knowledgePoliticsSociologyEnvironmental ethicsHistorySocial scienceMedicinePolitical scienceLawPathologyPhilosophy

Abstract

fetched live from OpenAlex

In this fascinating book, firmly grounded in a wide range of published source material, Anne Hardy traces the knowledge of Salmonella infections in humans and animals in the century after germ theory. Hardy covers a lot of terrain: new scientific theories, new scientific techniques and methods, new levels of state capacity and engagement, new kinds of gastronomic and hygienic tastes and habits, and last but certainly not least new ways of raising, processing, distributing, selling, preparing and even eating animals. She organises her material by dividing the book into three partially overlapping sections. The first—‘Pathways in Nature’—traces the slowly developing awareness of the various means beyond contaminated water and milk by which Salmonella caused disease. In successive chapters, Hardy explores the role of human carriers, shellfish, flies, duck eggs and other animals. These chapters are important for their role in the story of Salmonella food poisoning, but she also uses them to explore particular historical debates on, for example, the relevance of human carriers and the evidence for the role of flies in the transmission (and decline) of summer diarrhoea. Uniting all examples in this section is a focus on the significance of the laboratory for public health practice. Taking up Michael Worboys's call, Hardy shows that the laboratory was an ambiguous resource for public health practitioners.1 Tried and true epidemiological methods were the backbone of outbreak investigation, and at best the laboratory confirmed what had already been established by other means (p. 113). Bacteriology did not play an important until the mid-1930s (p. 36).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.049
GPT teacher head0.265
Teacher spread0.216 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2016
Admission routes1
Has abstractyes

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