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Record W4378834413 · doi:10.1177/00220094231178699

European Defence Planning: Concorde, Franco-British Politico-Military Relations and the Cold War, 1956–68

2023· article· en· W4378834413 on OpenAlexfundno aff
Glenn Wasson

Bibliographic record

VenueJournal of Contemporary History · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
FundersQueen's UniversitySociety for the Study of French History
KeywordsCold warContext (archaeology)General partnershipPolitical scienceHistoriographyLawEconomic historyHistoryPoliticsArchaeology

Abstract

fetched live from OpenAlex

Cold War tensions between the superpowers marked the years 1956 to 1966, where the United States, Britain and France prioritised European defence against Soviet aggression. Despite being envisaged for civil aviation purposes, the Concorde aircraft was the Franco-British alternative to US proposals concerning the introduction of a supersonic bomber within the Inter-Allied Nuclear Force (IANF) to protect against Soviet attack during the Cold War technological race. This offers a unique case study for the examination of Franco-British bi-lateral partnership in the context of European defence. This article will concentrate on three main themes. The first investigates how Concorde was considered as a viable component for the IANF. Subsequently, the loci of US, British and French policy decisions will be explored with regards to constructing supersonic aircrafts. Lastly, the article considers how foreign policies influenced the shift from a military-use Concorde to a more commercial option. The historiography on Concorde focuses on its commercial impact, and how it affected the Franco-British partnership. Considering Concorde from the defence perspective allows us to analyse the divisions between the US and French grand design ideas – the IANF and Europe puissance – and how they provoked further friction in the Franco-British military relationship.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.038
GPT teacher head0.210
Teacher spread0.172 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

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