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Record W2412635221

A Necessary Evil? Propaganda, Censorship, and Class in Britain's Ministry of Information, 1939-1941

2015· article· en· W2412635221 on OpenAlexaff
Conor Wilkinson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsWestern University
Fundersnot available
KeywordsCensorshipClass (philosophy)Christian ministryPolitical scienceLawComputer scienceArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

During the early phase of World War II, Britain’s Ministry of Information (MoI) instituted propaganda and censorship regimes that were shaped by British attitudes about class. The effectiveness of these regimes is debatable, especially in light of their continually hypocritical nature. Although the wartime context necessitated a high level of secrecy and guile within Whitehall, certain aspects of the Ministry’s campaigns were morally and politically questionable. A brief consideration of the historiography of British secrecy is followed by an analysis of the actions of the MoI and the Government generally from 1939 to 1941. The MoI failed to uphold the democratic ideals that it purported to represent in opposition to Nazi Germany. This failure manifested most obviously in the Ministry’s propaganda, its treatment of troops rescued from Dunkirk, and in its public addresses to the British public.\nCONOR WILKINSON is a fourth-year student at Huron University College with an Honours Specialization in History and a Minor in Geography. He will be attending the University of British Columbia to pursue a Master of Arts in British History in September 2015, where he will be working under the supervision of Dr. Joy Dixon.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.291

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0100.020
Scholarly communication0.0080.003
Open science0.0000.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.069
GPT teacher head0.257
Teacher spread0.188 · 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 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

Citations0
Published2015
Admission routes1
Has abstractyes

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Same topicHistorical Studies on Reproduction, Gender, Health, and Societal ChangesFrench-language works237,207