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Record W4362590369 · doi:10.29173/cons29494

Finding "Miss Canada"

2023· article· en· W4362590369 on OpenAlexaffvenueabout
Madalyn Mandziuk

Bibliographic record

VenueConstellations · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsNarrativeFront (military)Meaning (existential)Personal narrativePersonal lifeFront lineNursingFirst world warPsychologyHistoryMedia studiesSociologyPolitical scienceMedicineLaw

Abstract

fetched live from OpenAlex

During the First World War, many Canadian women served on the front lines as nurses, both as working professionals and volunteers. Although the experiences of Canadian women during WWI have been addressed by historians more frequently in recent decades, the experience of professional nurses has been overshadowed by that of Volunteer Aid Detachment nurses (VADs) and women on the home front and of course by studies on soldiers’ experiences. Many historians have found the personal narrative, diary, or journal to be an invaluable source to understanding the First World War. However, there is a gap in the study of the personal narratives, diaries, and journals of Canadian professional nurses specifically. This paper seeks to bridge this gap in WWI history through a specific focus on a selection of the surviving personal papers of Canadian professional nurses. Their personal writings reveal new insights into nursing experience on the front, nursing work, and how Canadian professional nurses sought and found meaning in extraordinary and violent circumstances. They made sense of the war through sociability, relationality, and through recording their time overseas both to cope with the experience itself, and to remember it upon their return home.

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)0.004
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.399

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0420.007
Scholarly communication0.0080.002
Open science0.0010.005
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0210.002

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 head0.031
GPT teacher head0.265
Teacher spread0.234 · 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
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
Published2023
Admission routes3
Has abstractyes

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