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Record W1504036220 · doi:10.1162/jinh.2000.31.2.254

<i>The Pity of War: Explaining World War I</i>. By Niall Ferguson (New York, Basic Books, 1999) 563 pp. $30.00

2000· article· en· W1504036220 on OpenAlexaff
John Ferris

Bibliographic record

VenueThe Journal of Interdisciplinary History · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicItalian Fascism and Post-war Society
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPityIconCitationDownloadWorld War IIHistoryWorld Wide WebLibrary scienceComputer scienceArtLiteratureArchaeology

Abstract

fetched live from OpenAlex

October 01 2000 The Pity of War: Explaining World War I The Pity of War: Explaining World War I. By NiallFerguson (New York, Basic Books, 1999) 563 pp. $30.00 John Ferris John Ferris Search for other works by this author on: This Site Google Scholar Author and Article Information John Ferris Online Issn: 1530-9169 Print Issn: 0022-1953 © 2000 Massachusetts Institute of Technology2000 The Journal of Interdisciplinary History (2000) 31 (2): 254–256. https://doi.org/10.1162/jinh.2000.31.2.254 Cite Icon Cite Permissions Share Icon Share MailTo Twitter LinkedIn Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Search Site Citation John Ferris; The Pity of War: Explaining World War I. The Journal of Interdisciplinary History 2000; 31 (2): 254–256. doi: https://doi.org/10.1162/jinh.2000.31.2.254 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll JournalsThe Journal of Interdisciplinary History Search Advanced Search This content is only available as a PDF. © 2000 Massachusetts Institute of Technology2000 Article PDF first page preview Close Modal You do not currently have access to this content.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.338
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.020
GPT teacher head0.265
Teacher spread0.245 · 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 teacher head, not a consensus.

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

Citations0
Published2000
Admission routes1
Has abstractyes

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