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Record W1525978064 · doi:10.1017/cbo9780511642371.008

Conclusions

2009· book-chapter· en· W1525978064 on OpenAlexaff
Holger H. Herwig

Bibliographic record

VenueCambridge University Press eBooks · 2009
Typebook-chapter
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Why did none of the “war plans” of 1914 succeed? Why did they all prove to be inadequate in some way or another? Why did the much-anticipated Armageddon not occur either in Galicia or in northern France? And why were the troops not home by Christmas after the “short, cleansing thunderstorm” predicted by both popular and military writers? The authors of the foregoing essays have addressed these questions through evaluations of the individual war plans of the great powers in 1914. War plans, in the inimitable words of historian Dennis Showalter, were “the nineteenth-century intelligence equivalent of the medieval knight's Holy Grail.” “The history of the campaign of 1914 is nothing else but the story of the consequences of the strategical errors of the War Plan.” With these critical words, General N. N. Golovin sought to explain the Russian debacle of 1914. There obviously was no doubt in his mind that war plans existed – at least in Russia – on the eve of the July Crisis 1914 and that the opening moves of World War I were executed by the great captains on the basis of the plans at hand. In Germany, Staff Chief General Wilhelm Groener after the war spoke of the “symphony” of the Schlieffen Plan and of the “conductor” (Helmuth von Moltke the Younger) who “bungled” its execution. For the historian of today, the task is not so simple nor the path so straightforward.

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)0.022
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.183
Threshold uncertainty score0.612

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0060.004
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.1830.054

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 head0.022
GPT teacher head0.232
Teacher spread0.210 · 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
GenreOther

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
Published2009
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicWorld Wars: History, Literature, and ImpactFrench-language works237,207