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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 OpenAlexaff
Daniel Beltranena

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.

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)0.006
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0100.012
Scholarly communication0.0070.003
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.000

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

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 designObservational
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

Citations1
Published2021
Admission routes1
Has abstractyes

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