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Record W1423244534 · doi:10.1017/s0025727300000454

Michael G. Tyshenko, SARS Unmasked: Risk Communication of Pandemics and Influenza in Canada, McGill-Queen's/Associated Medical Health Services Studies in the History of Medicine, Health and Society (Montreal: McGill-Queen's University Press, 2010), pp. xii + 451, $34.95, paperback, ISBN: 978-0-7735-36180.

2012· article· en· W1423244534 on OpenAlexaboutno aff
Virgínia Berridge

Bibliographic record

VenueMedical History · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicHistory of Science and Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsQueen (butterfly)PandemicHistory of medicineCoronavirus disease 2019 (COVID-19)MedicineGerontologyMedia studiesLibrary scienceSociologyComputer scienceInfectious disease (medical specialty)PathologyDiseaseBiology

Abstract

fetched live from OpenAlex

Michael G. Tyshenko, SARS Unmasked: Risk Communication of Pandemics and Influenza in Canada, McGill-Queen's/Associated Medical Health Services Studies in the History of Medicine, Health and Society (Montreal: McGill-Queen's University Press, 2010), pp. xii + 451, $34.95, paperback, ISBN: 978-0-7735-36180. - Volume 56 Issue 1

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)0.004
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.690

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0050.004
Scholarly communication0.0060.007
Open science0.0020.002
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0350.011

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 head0.039
GPT teacher head0.256
Teacher spread0.217 · 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 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

Citations1
Published2012
Admission routes1
Has abstractyes

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