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Record W7008206466

Canadian Prisoners of the First World War: The Struggle for Resilience

2022· article· en· W7008206466 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWorld Wars: History, Literature, and Impact
Canadian institutionsnot available
Fundersnot available
KeywordsNothingPrisonGermanNarrativeMemoirGulagPsychological resilienceFront (military)Resilience (materials science)
DOInot available

Abstract

fetched live from OpenAlex

In the First World War, 3,500 Canadian soldiers were taken prisoner. Throughout their captivity, they endured intense humiliation, dehumanization, and abuse. Despite this, the men were able to remain resilient and even found ways to fight back. By using memoirs and letters written by the prisoners, this paper will analyze how these Canadians were determined to keep fighting. This paper will be using an analogy of a bank account to explain how close the prisoners came to breakdown, and how they continuously struggled to endure. Society and war had taught these men that prisoners were weak and cowardly, but they were determined to change this narrative and prove their own bravery through decisive actions of physical and mental resistance, evasion, and escape. By all accounts, the prisoners should have run out of their morale reserves, they should have gone past the breaking point of war weariness to complete breakdown, and they should have had nothing left in them to endure. But the foundation of camaraderie they had built on the front lines set the Canadian soldiers up to endure trauma, remain resilient, and continue their own fight while in the prison camps of Germany. The purpose of this paper is to give a voice to Canadian prisoners of the First World War, and to use the concept of resilience to understand their determination to continue their fight in German territory.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
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
Bibliometrics0.0000.001
Science and technology studies0.0040.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.297
Teacher spread0.248 · 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 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
Published2022
Admission routes1
Has abstractyes

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