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Record W2035420253 · doi:10.4000/amnis.1402

D’allié à ennemi. Stéréotypes et représentations du combattant russe dans les magazines illustrés français durant la Grande Guerre

2011· article· fr· W2035420253 on OpenAlexaff
Raymond Blanchard, Joceline Chabot, Sylvia Kasparian

Bibliographic record

VenueAmnis · 2011
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsHumanitiesArtCollective memoryPolitical science

Abstract

fetched live from OpenAlex

Les recherches récentes en histoire sociale et culturelle sur la Grande Guerre s’intéressent notamment à l’étude de la production littéraire, médiatique et iconographique comme pratique signifiante et représentative de l’expérience individuelle et collective de la guerre, ce que les historiens identifient comme les « cultures de guerres ». Ces « cultures de guerre » sont analysées, entre autres, à partir de la presse, dont la situation privilégiée au carrefour de l’opinion publique et de la propagande gouvernementale, en fait un objet d’étude significatif pour mieux comprendre la mobilisation des populations dans le cadre d’une guerre totale. À partir d’une approche interdisciplinaire, notre article a pour principal objectif de dégager et d’analyser les stéréotypes qui règlent les représentations de l’allié russe dans la presse illustrée française durant la Grande Guerre. Plus précisément, nous voulons interroger l’évolution des représentations de la figure du combattant russe afin de dégager dans quelle mesure il y a eu rupture ou permanence des représentations, en fonction de l’évolution du contexte historique entre 1914 et 1920.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0080.008
Scholarly communication0.0040.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.033
GPT teacher head0.247
Teacher spread0.214 · 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 designQualitative
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
Published2011
Admission routes1
Has abstractyes

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