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Record W4380271355 · doi:10.1515/9780773582774

Fatal Glamour

2015· book· en· W4380271355 on OpenAlexaff
Paul Delany

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2015
Typebook
Languageen
FieldArts and Humanities
TopicHistorical Studies on Reproduction, Gender, Health, and Societal Changes
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsArt

Abstract

fetched live from OpenAlex

Rupert Brooke (b. 1887) died on April 23, 1915, two days before the start of the Battle of Gallipoli, and three weeks after his poem "The Soldier" was read from the pulpit of St Paul's Cathedral on Easter Sunday. Thus began the myth of a man whose poetry crystallizes the sentiments that drove so many to enlist and assured those who remained in England that their beloved sons had been absolved of their sins and made perfect by going to war. In Fatal Glamour, Paul Delany details the person behind the myth to show that Brooke was a conflicted, but magnetic figure. Strikingly beautiful and able to fascinate almost everyone who saw him - from Winston Churchill to Henry James - Brooke was sexually ambivalent and emotionally erratic. He had a series of turbulent affairs with women, but also a hidden gay life. He was attracted by the Fabian Society’s socialist idealism and Neo-Pagan innocence, but could be by turns nasty, misogynistic, and anti-Semitic. Brooke’s emotional troubles were acutely personal and also acutely typical of Edwardian young men formed by the public school system. Delany finds a thread of consistency in the character of someone who was so well able to move others, but so unable to know or to accept himself. A revealing biography of a singular personality, Fatal Glamour also uses Brooke’s life to shed light on why the First World War began and how it unfolded.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0140.004
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0040.009
Insufficient payload (model declined to judge)0.0730.024

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 head0.055
GPT teacher head0.226
Teacher spread0.171 · 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 designTheoretical or conceptual
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

Citations0
Published2015
Admission routes1
Has abstractyes

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