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Record W1563043852

The Balance Sheet: The Costs and the Gains of the Bombing Campaign

2006· article· en· W1563043852 on OpenAlexaff
David L. Bashow

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicDefense, Military, and Policy Studies
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsBalance (ability)Operations managementBusinessOperations researchEconomicsEngineeringMedicinePhysical medicine and rehabilitation
DOInot available

Abstract

fetched live from OpenAlex

Critics of the bomber offensive frequently argue that the material and human cost of the campaign far overshadowed the gains, and that the resources dedicated to it could have been more effectively utilized elsewhere. They have argued that the combat manpower could have been better used in the other fighting services, especially the army, and industry could have been used to produce more weapons for these fighting services. However, proponents of this line of thought assume that the weight of effort expended on the bombing campaign was inordinately high. Richard Overy maintains that it was actually rather modest. “Measured against the totals for the entire war effort (production and fighting), bombing absorbed 7 percent, rising to 12 percent in 1944–45. Since at least a proportion of bomber production went to other theatres of war, the aggregate figures for the direct bombing of Germany were certainly smaller than this. Seven percent of Britain’s war effort can hardly be regarded as an unreasonable allocation of resources.” Further, although some significant infantry shortages were experienced in 1944, they never reached an extremely critical overall level and were eventually rectified. With respect to materiel, none of the services was conspicuously wanting for anything by 1943, and the British effort was thereafter bolstered by substantial North American war production.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.190
Teacher spread0.175 · 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 designTheoretical or conceptual
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

Citations1
Published2006
Admission routes1
Has abstractyes

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