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Record W2041799243 · doi:10.3138/9781442693487

Boys and Girls in No Man's Land: English-Canadian Children and the First World War

2011· book· en· W2041799243 on OpenAlexaffabout
Susan Fisher

Bibliographic record

VenueProject Muse (Johns Hopkins University) · 2011
Typebook
Languageen
FieldArts and Humanities
TopicThemes in Literature Analysis
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsPatriotismAdventureDutyWorld War IISacrificeTone (literature)Representation (politics)Spanish Civil WarFirst world warHistoryFace (sociological concept)Gender studiesPolitical scienceSociologyLawLiteratureSocial sciencePoliticsArtAncient historyArt history

Abstract

fetched live from OpenAlex

Boys and Girls in No Man's Land examines how the First World War entered the lives and imaginations of Canadian children. Drawing on educational materials, textbooks, adventure tales, plays, and Sunday-school papers, this study explores the role of children in the nation's war effort.Susan R. Fisher also considers how the representation of the war has changed in Canadian children's literature. During the war, the conflict was invariably presented as noble and thrilling, but recent Canadian children's books paint a very different picture. What once was regarded a morally uplifting struggle, rich in lessons of service and sacrifice, is now presented as pointless slaughter. This shift in tone and content reveals profound changes in Canadian attitudes not only towards the First World War but also towards patriotism, duty, and the shaping of the moral citizen

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 score0.053
Threshold uncertainty score0.388

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0360.011
Scholarly communication0.0070.003
Open science0.0020.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.009
GPT teacher head0.167
Teacher spread0.157 · 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

Citations27
Published2011
Admission routes2
Has abstractyes

Explore more

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