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Record W2330925940 · doi:10.1017/s1537781414000784

DOUBLE-VOICED: MUSIC, GENDER, AND NATURE IN PERFORMANCE

2015· article· en· W2330925940 on OpenAlexaff
David Monod

Bibliographic record

VenueThe Journal of the Gilded Age and Progressive Era · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsSingingTransgressiveSilenceContext (archaeology)EntertainmentPerforming artsArgument (complex analysis)Variety (cybernetics)Tone (literature)Intonation (linguistics)Identity (music)Vocal musicHistoryPsychologyLiteratureLinguisticsAestheticsArtVisual artsMusicAcousticsMusic educationPhilosophy

Abstract

fetched live from OpenAlex

Abstract Double-voiced singing was a popular form of variety show entertainment from the 1860s through to the 1920s. Double-voiced performers were able, through intonation and tone, to sound as though they had at least two separate and distinct “voices,” generally one soprano and one baritone. But as Claire Rochester, a double-voiced singer of the early twentieth century made clear, their act was more than just a matter of a woman singing low notes or a man singing high ones; it was all about a performer adopting the “voice” of the other sex. The unusual practice of these singers was to sing duets (and sometimes as much as quartets) to themselves and by themselves, flipping back and forth between their male to female “voices.” I place this strange form of entertainment in the context of changing attitudes to gender and sexuality and suggests that conventional interpretations of “freak” performances as “transgressive” fail to account for these vocal wonders. Double-voiced singers shunned the “transgressive” billing, especially when their own sexual identity was called into question. In making this argument, I suggest that we need to widen our understanding of “freakery,” imposture and the meaning of “nature” and “truth,” as they were revealed both on stage and off.

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 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 score0.798
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.058
GPT teacher head0.242
Teacher spread0.184 · 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 teacher head, 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

Citations13
Published2015
Admission routes1
Has abstractyes

Explore more

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