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Record W2033197221 · doi:10.1177/0022009405054564

HMG and the War against Dissent, 1914-18

2005· article· en· W2033197221 on OpenAlexaff
Brock Millman

Bibliographic record

VenueJournal of Contemporary History · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsWestern University
Fundersnot available
KeywordsDissentFront (military)World War IIPolitical scienceFirst world warSpanish Civil WarHome frontLawHistoryPolitical economySociologyAncient historyPoliticsEngineering

Abstract

fetched live from OpenAlex

Total war is not simply a clash of forces but a content of societies. Defeat is almost never solely the consequence of events at the front, but develops from a complex of failures. In the case of the first world war social dislocation not only followed but preceded defeat. Understanding how dissent was moderated is of crucial importance to an understanding of why the first world war ended as it did. This article considers how dissent was combated in the first world war in general and particularly in Britain.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0100.019
Scholarly communication0.0060.002
Open science0.0000.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.028
GPT teacher head0.273
Teacher spread0.244 · 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 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

Citations11
Published2005
Admission routes1
Has abstractyes

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