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Record W3217576593

“Carry On”: State Censorship and Denial of Spanish Influenza in Great Britain (1918-19)

2021· article· en· W3217576593 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStudent Research Proceedings · 2021
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsMacEwan University
Fundersnot available
KeywordsCensorshipDenialPandemicParallelsAdversaryState (computer science)PopulationHistoryPolitical scienceWorld War IILawEconomic historyDemographySociologyCoronavirus disease 2019 (COVID-19)MedicineInfectious disease (medical specialty)DiseasePsychology
DOInot available

Abstract

fetched live from OpenAlex

In the final year of the “war to end all wars”, the world would be plagued by a new universal enemy: Spanish Influenza. Considered the largest pandemic of all time, in terms of infection and death rates, the 1918-1920 virus is estimated to have affected half of the world’s population and killed 50-100 million. However, for as cataclysmic as this disease was, it has often been forgotten by both academia and society as a whole. Using Great Britain and Ireland as an example, it is argued that the Spanish Flu has largely been forgotten as a result of state official denial and press censorship in a time when the country could not afford to look weak in the final year of World War I and its immediate aftermath. Parallels are drawn to the current COVID-19 pandemic. Department: Interdisciplinary Dialogue Project Faculty Mentor: Dr. Aidan Forth

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.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.273
GPT teacher head0.504
Teacher spread0.231 · 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