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Record W1495406097

Strategic Bombing if Possible, but Possibly not Strategic Bombing: an examination of ends, ways, and means and the use of strategic airpower during the Great War

2015· article· en· W1495406097 on OpenAlexvenueaboutno aff
Randall Wakelam

Bibliographic record

VenueJournal of military and strategic studies · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNavyStrategic bombingAdversaryContext (archaeology)HappeningStrategic planningPolitical scienceMilitary strategyStrategic goalPublic relationsManagementWorld War IILawMilitary scienceHistoryComputer scienceComputer securityEconomics
DOInot available

Abstract

fetched live from OpenAlex

This paper is an offshoot of research conducted in preparation for the University of Calgary History conference of 2014 focussing on new perspectives of the Great War.  My primary intent in that research was to explore the notion that air services were, using the recent educational concept of the Learning Organization, in fact precursors of this concept within a military context.  One of the conclusions I came to is that this learning was not just happening within the air services but took place even at the national, or grand strategic, level where decisions had to be made both about how to use this new means of warfare and about the allocation of resources while continuing to support the needs of the army and navy.  The former had to do with strategic bombing of enemy targets and the balance of this paper looks at how the concepts and practice of strategic bombing evolved in France, Germany and 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)0.004
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 score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.023
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0010.002
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 head0.158
GPT teacher head0.259
Teacher spread0.100 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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