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Record W2465530095 · doi:10.7202/1035601ar

L’humour des Poilus canadiens durant la Grande Guerre (première partie)

2016· article· fr· W2465530095 on OpenAlexvenueaboutno aff
Bernard Andrès

Bibliographic record

VenueLes Cahiers des dix · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Malgré la crise de la conscription de 1917 et les tensions entre anglophones et francophones, au sujet de l’engagement du Canada dans la Grande Guerre, plus de 30 000 Canadiens français partent pour le front. Ils s’illustreront notamment aux batailles d’Ypres, de Courcelette et de Vimy. Chez ces francophones dont la plupart se battent sous le drapeau britannique, on note une certaine ambivalence face « la » patrie (le Canada ? l’Angleterre ? la France ?). Défiance également envers la hiérarchie militaire et les autorités coloniales britanniques. De rares témoignages de première main publiés en français entre 1914 et 1920 permettent d’apprécier sous un angle nouveau —l’humour— cet épisode de l’histoire québécoise. Face à la censure de guerre et pour conjurer la mort, les récits recourent à ces stratégies d’évitement ou de subversion que sont l’humour, l’ironie et le sarcasme. On observe ces modes d’écriture dans les témoignages de quelques « Poilus » canadiens publiés entre 1914 et 1920. La majorité d’entre eux servent sous l’uniforme anglais : Henri Chassé, Claudius Corneloup, Arthur J. Lapointe, A. et W. Audette, Joseph A. Lavoie et Moïse E. Martin. Paul Caron (le seul à mourir au front), s’est engagé, lui, dans l’Armée française : cet ardent nationaliste affirmait se battre pour la France et s’opposer au « navalisme et à l’impérialisme britanniques ».

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)0.001
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 score0.178
Threshold uncertainty score0.359

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0180.006
Scholarly communication0.0050.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.002

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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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Same venueLes Cahiers des dixSame topicCanadian Identity and HistoryFrench-language works237,207