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Record W7056976515

Grief Amidst the Guns: Death in the Canadian Expeditionary Force on the Western Front

2024· article· en· W7056976515 on OpenAlexaboutno aff

Bibliographic record

VenueScholars Commons (Wilfrid Laurier University) · 2024
Typearticle
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsGriefFront (military)AngerCoping (psychology)AdversaryFace (sociological concept)World War IITerrorism
DOInot available

Abstract

fetched live from OpenAlex

This dissertation examines how Canadian soldiers of the First World War dealt with death on the Western Front. Members of the Canadian Expeditionary Force risked death, witnessed it and lived alongside it while serving in the trenches. Circumstances of death in the frontlines differed from those in civilian life; death of the very young by disease was replaced by violent death of adult men. Even conflicts of the preceding fifty years provided little indication of what soldiers would face during the First World War. Canada’s death rate during the First World War was nearly three times that of the South African War and the vast majority of those deaths were due to enemy action. This dissertation argues that the scale and violence of death on the Western Front pushed soldiers to develop multiple means of coping and grieving. Reactions varied; emotional reserve, sadness, communal grieving and gravesite rituals appear in men’s writings alongside anger and dark humour in response to the deaths of comrades. Even as the Western Front presented new and challenging circumstances, the men of the Canadian Expeditionary Force often turned to and adapted their civilian death customs to manage their emotions and experiences with the dead.

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: Other · Consensus signal: none
Teacher disagreement score0.089
Threshold uncertainty score0.202

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.007
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.201
Teacher spread0.186 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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