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Record W2886971206 · doi:10.7765/9781526101532.00016

Eyewitnesses to revolution

2015· book-chapter· en· W2886971206 on OpenAlexaboutno aff
Cynthia Toman

Bibliographic record

VenueManchester University Press eBooks · 2015
Typebook-chapter
Languageen
FieldPsychology
TopicHistorical Psychiatry and Medical Practices
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipFront lineRussian revolutionSituatedCultural revolutionPolitical scienceHistoryEconomic historyAncient historyLawChina

Abstract

fetched live from OpenAlex

This chapter extends the scholarship by examining the nature of military nurses' work as a form of gendered imperialism, as politically situated, and as diplomatically sensitive. Four Canadian Army Medical Corps (CAMC) nursing sisters served at the Anglo-Russian Hospital between its founding in November 1915 and closure in January 1918. The 1917 Russian Revolution, however, compounded the chaos of war and disrupted the formerly close associations between Russia and England. Dorothy Cotton's front-line experience was cut short, however, by a leave to Canada for several months, following the deaths of her two brothers in France. It was on her return to the Anglo-Russian Hospital at the end of 1916, that hospital staff found themselves in a vantage position as eyewitnesses to the beginnings of the Russian Revolution in Petrograd. England was particularly sensitive to Russian demands for Allied support and responded by establishing a joint Anglo-Russian hospital unit at Petrograd.

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)0.000
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: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.008
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0150.003

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 head0.100
GPT teacher head0.299
Teacher spread0.198 · 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
Published2015
Admission routes1
Has abstractyes

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