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Record W4392370294

Pour qui combattre? : les volontaires canadiens-français entre l’affirmation et l’indifférence, 1914-1919

2021· preprint· fr· W4392370294 on OpenAlexaboutno aff
Michel Litalien

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2021
Typepreprint
Languagefr
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In the case of Canada, the idea that Francophones did not show the same enthusiasm for enlistment as the country's Anglophones dominates. Until the adoption of conscription in December 1917 and its implementation in the winter of 1918, French Canadians did, however, enlist voluntarily and in greater numbers than the estimates put forward by an American historian in 1937, figures still used today. Patriotism was not the main reason for enlisting French-Canadian volunteers, but a taste for adventure was probably the most common. From a historiographical point of view, the participation of French-Canadian volunteers at the front is generally limited to the 22nd Infantry Battalion, a unit that distinguished itself many times at the front. Yet there were enough French-speaking volunteers to form and maintain other units at the front, or even a complete French-speaking infantry brigade. Instead, the military authorities ordered that "surplus" French-speaking soldiers be sent to reinforce the 22nd Battalion, but above all to the Anglo-Quebec units, which were also suffering from a shortage of volunteers. Fighting thousands of kilometres from their homes in defence of a country that was not theirs, a minority in an English-speaking army that was not very welcoming to minorities, the target of an English-speaking press that was sometimes hostile to them and a population that was generally hostile to conscription, for whom were these soldiers fighting this war? Beyond this question, our thesis examines the contribution and wartime experience of Francophone combatants. It examines the interactions between the latter and their English-speaking counterparts and also the relations with the Belgian and French populations. This thesis is part of both a national Canadian history of the First World War, because of the similarities between the two linguistic national groups, and a 'transnational' history in which the experiences of the different minority communities could be compared. In addition to the exploitation of military archives, our thesis is based on the cross-analysis of published and unpublished testimonies of combatants, of which this work also provided the opportunity to produce a reasoned inventory. Except for the French Canadian opposition to the conscription Canadian publications ignore almost everything of the French Canadian war experience. When mentioned it usually relies on preconceived ideas developped during the war. Did the French Canadian soldiers live a similar or a different war experience from the English speaking Canadian soldiers? Like the the citizens in Canada were they divided as well? Past that highly critical and sensitive issue of contemporary Canadian history, we will analyze the French Canadian experience and compare it with the English-speaking components of the Canadian Expeditionary Force. Besides making use of military archives, our doctoral research will be based on a cross analysis of the published and unpublished recollections of combatants, for which this work will also be an opportunity to develop a systematic inventory.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.481

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0300.015
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0100.001

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 head0.013
GPT teacher head0.220
Teacher spread0.207 · 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 designObservational
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
Published2021
Admission routes1
Has abstractyes

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