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Record W1485859098 · doi:10.3138/cbmh.30.1.143

“Help Us, Serve England”: First World War Military Nursing and National Identities

2013· article· en· W1485859098 on OpenAlexaffvenueabout
Cynthia Toman

Bibliographic record

VenueCanadian Journal of Health History · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSisterQueen (butterfly)Military serviceWorld War IIService (business)First world warIdentity (music)Gender studiesPolitical scienceNursingHistorySociologyMedicineLawAncient historyArt

Abstract

fetched live from OpenAlex

Historians generally argue that the First World War was a defining experience from which Canadians emerged with a strong sense of national identity distinct from their British roots. There is little historical research on women's wartime experiences and even less on military nurses. This article explores the working relationships of Nursing Sister Emeline Robinson with British nurses, VAD volunteers, orderlies, and medical officers during her one and a half years with the Queen Alexandra Imperial Military Nursing Service Reserve through her diary, which spans her enlistment, resignation, and re-enlistment with the Canadian Army Medical Corps.

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, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.727
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
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.243
Teacher spread0.221 · 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 designNot applicable
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

Citations7
Published2013
Admission routes3
Has abstractyes

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