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Record W4392050286 · doi:10.1515/9781787446779

Crippen

2020· book· et· W4392050286 on OpenAlexaboutno aff
Roger Dalrymple

Bibliographic record

VenueBoydell and Brewer eBooks · 2020
Typebook
Languageet
FieldArts and Humanities
TopicCrime and Detective Fiction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

How did the case of the 'mild mannered murderer', Hawley Harvey Crippen, come to have such an enduring cultural resonance? Almost as notorious as Jack the Ripper, US citizen and homeopath Dr Hawley Harvey Crippen was forty-eight years old when he was hanged in London in November 1910 for the murder and mutilation of his wife. When Cora Crippen vanished in February 1910, he claimed that she had returned to the United States. Yet the discovery of a dismembered body, buried beneath the cola cellar of their house, and Crippen's attempt to flee to Canada with his cross-dressed mistress exposed and convicted him. The case aroused enormous public interest at the time, and it has remained in the popular imagination ever since, memorialised in crime history, fiction, film and even musical theatre. As late as 2007, some American academics were claiming that the dead body was not Cora's and that Crippen was in fact innocent. This book aims to account for the endurance of the Dr Crippen murder case in the cultural imagination. Highlighting the case's disruptive blending of cultural traditions, it discusses historical precedents, analyses diverse literary traditions, looks at broadside balladry and music-hall repertoire and addresses queer theory discourses. The book shows how the case, part throwback to earlier crime sensations and part presage of a new understanding of criminality, represents a watershed in the representation of criminality and played a distinctive role in the development of crime fiction. ROGER DALRYMPLE is a Principal Lecturer in Education at Oxford Brookes University.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.319
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.052
GPT teacher head0.218
Teacher spread0.166 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2020
Admission routes1
Has abstractyes

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