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Record W4391765281 · doi:10.3366/cfs.2024.0107

‘The bloody fingers … bear witness’: Sign Language and the Mute Detective in <i>Susan Hopley</i> and <i>The Trail of the Serpent</i>

2024· article· en· W4391765281 on OpenAlexaff
Emily M. Cline

Bibliographic record

VenueCrime Fiction Studies · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicCrime and Detective Fiction Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsWitnessSerpent (symbolism)BloodySign (mathematics)ArtLiteratureHistoryAncient historyLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

In her examination of signing characters in works of Charles Dickens and Wilkie Collins, Jennifer Esmail highlights deaf characters’ absence in Victorian fiction. Mutism is more common, for example, in the character of Mary Elizabeth Braddon's working-class, fingerspelling Detective Peters (70, 10n24). Even so, sign language – seen as ‘primitive’ and ‘lacking [in] intellectual […] rigor’ – was rarely represented (Esmail 3). Dickens, Collins and Braddon, proto-detective novelists themselves, were preceded by Catherine Crowe, whose 1841 novel Susan Hopley features Julie le Moine, a female, cross-dressing sleuth whose mutism does not prevent her from testifying using ‘signs’ and ‘the finger alphabet’ (III.100, 125). Julie's undercover work not only crosses class, gender, and genre boundaries, but her non-verbal evidence challenges women's exclusion from ‘“masculine” systems of representation’ symbolised by legal parlance (Irigaray 85). When detective fiction pits the private eye against traditional jurisprudential structures, the courtroom becomes a space where classed and gendered hierarchies inform testimonial evidence, reinforcing exclusionary principles that disenfranchise the ‘Other’. The sign language, fingerspelling and lip-reading of Julie le Moine, Joseph Peters and Richard Marsh's Judith Lee expose ableist and sexist barriers that seek to expunge the detectives from the legal record by excluding them from public speech.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.266
Teacher spread0.241 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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