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Record W4403601769 · doi:10.4000/12gxs

Les airgraphs comme support médiatique. Microfilm, aviation et culture de la communication transnationale en Grande‑Bretagne lors de la Seconde Guerre mondiale

2023· article· fr· W4403601769 on OpenAlexaff
Emily Doucet

Bibliographic record

VenueTransbordeur. · 2023
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsSocial Sciences and Humanities Research Council
Fundersnot available
KeywordsMicroformHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Le airgraph est un format photographique résultant de la mise sur microfilm de lettres ou de documents initialement rédigés sur des formulaires types. Sa fonction était de libérer de l’espace dans les avions-cargos qui empruntaient les voies postales transnationales. Breveté par l’entreprise Eastman Kodak, la société de transport aérien long courrier britannique Imperial Airways et la compagnie aérienne américaine Pan Am Airways, ce système a d’abord été développé à grande échelle par la Grande-Bretagne pendant la Seconde Guerre mondiale. Cet article revient sur une série de films, d’affiches et de publicités commerciales destinés à promouvoir ce système auprès de ses utilisateurs, et avance que ces supports visuels ont joué un rôle déterminant dans ce nouveau mode de communication. Ces représentations illustrent de façon concrète l’association entre les deux moyens de communication que sont la photographie et l’aviation, lien créé pour bâtir la confiance des populations envers la gestion étatique des communications transnationales. Ainsi, les airgraphs sont devenus un motif qui a alimenté un imaginaire entourant ce que la connexion, ou l’absence de connexion, signifiait pour l’Empire britannique pendant la Seconde Guerre mondiale.

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: none
Teacher disagreement score0.045
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0070.004
Open science0.0000.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0450.005

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 head0.023
GPT teacher head0.289
Teacher spread0.266 · 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
Published2023
Admission routes1
Has abstractyes

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