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Record W2972894392 · doi:10.1177/0306197319870372

Fictional German governesses in Edwardian popular culture: English responses to German militarism and modernity

2019· article· en· W2972894392 on OpenAlexaff
Susan N. Bayley

Bibliographic record

VenueLiterature & History · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsDawson College
Fundersnot available
KeywordsMilitarismModernityGermanAmbivalenceLiteratureArtArt historyHistoryPsychoanalysisPoliticsPsychologyLawPolitical science

Abstract

fetched live from OpenAlex

Historians have tended to focus on propaganda when assessing Edwardian attitudes towards Germans, but a shift of focus to fiction reveals a rather different picture. Whereas propaganda created the cliché of ‘the Hun’, fiction produced non- and even counter-stereotypical figures of Germans. An analysis of German governess characters in a selection of short stories, performances, novels, and cartoons indicates that the Edwardian image of Germans was not purely negative but ambivalent and multifarious. Imagined German governesses appeared as patriots and spies, pacifists and warmongers, spinsters and seducers, victims and evil-doers. A close look at characterisations by Saki [H. H. Munro], M. E. Francis [Margaret Blundell], Dorothy Richardson, D. H. Lawrence, Radclyffe Hall, Frank Hart and others reveals not only their variety but also their metaphorical use as responses to Germany’s aggressive militarism and avant-garde modernity. Each governess figure conveyed a positive, negative or ambivalent message about the potential impact of German militarism and modernity on England and Englishness. The aggregate image of German governesses, and by inference Germans, was therefore equivocal and demonstrates the mixed feelings of Edwardians toward their ‘cousin’ country.

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)0.002
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0050.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.023
GPT teacher head0.248
Teacher spread0.225 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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