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Record W627649221 · doi:10.1017/cbo9780511804854

Decisions for War, 1914–1917

2004· book· en· W627649221 on OpenAlexaff
Richard F. Hamilton, Holger H. Herwig

Bibliographic record

VenueCambridge University Press eBooks · 2004
Typebook
Languageen
FieldSocial Sciences
TopicGlobal Political and Social Dynamics
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOttoman empireEconomic historyEmpireAncient historyPolitical scienceHistoryFirst world warGeographyLawPolitics

Abstract

fetched live from OpenAlex

Decisions for War focuses on the choices made by small coteries in Austria-Hungary, Germany, Russia, France, Britain and elsewhere to address a common yet perplexing question: why did World War I happen? Several of the usual causes for the war are reviewed and discussed. Rather than accepting arguments of mass demands, nationalism, militarism, and social Darwinism, the book shows how in each country, the decision to enter the war was made by only a handful of individuals - monarchs, ministers, military people, party leaders, ambassadors, and others. In each case, we also see separate and distinct agendas, the considerations differing from one nation to the next. The leadership of Japan, the Ottoman Empire, Italy, the Balkans, and the United States are explored, as well as that of the major European countries involved.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.022
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0130.004

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 head0.031
GPT teacher head0.261
Teacher spread0.230 · 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

Citations17
Published2004
Admission routes1
Has abstractyes

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