MétaCan
Menu
Back to cohort
Record W2165496101 · doi:10.1017/s0018246x09007523

STRATEGIC BOMBING AND RESTRAINT IN ‘TOTAL WAR’, 1915–1918

2009· article· en· W2165496101 on OpenAlexaff
Andrew Barros

Bibliographic record

VenueThe Historical Journal · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsStrategic bombingHome frontPoliticsTotal warFront (military)Government (linguistics)Political scienceWorld War IIPolitical economyFirst world warStrategic studiesMilitary strategyStrategic goalSpanish Civil WarHistoryMilitary scienceLawSociologyEngineeringAncient history

Abstract

fetched live from OpenAlex

ABSTRACT Recent studies of ‘total war’ depict a process of inexorable expansion leading to an often nebulous linkage of everything to war. This article takes the study of ‘total war’ in the opposite direction by studying a specific example of strategic restraint. It examines how the French bombing strategy that was developed over the course of the First World War went to considerable lengths to maintain a distinction between the civilian and the military. The article studies France's restraint by highlighting the strategic, geographical, institutional, and economic factors upon which it was built. It then goes on to examine the political pressures for an expansion of bombing which proved incapable of overturning this policy. Finally, it contrasts French restraint with that of its key ally, Great Britain. There, bombing developed into a strategic weapon designed to destroy the ‘home front’. This study of restraint underscores the importance of limits, and the attendant choices government has to make, in understanding the course and intensity of a country's mobilization for modern war.

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: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.012
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.031
GPT teacher head0.283
Teacher spread0.252 · 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

Citations3
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueThe Historical JournalSame topicWorld Wars: History, Literature, and ImpactFrench-language works237,207