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Teaching Silence in the Twenty-First Century

2015· book· en· W288860728 on OpenAlexaff
Roxane Prevost, Kimberly Francis

Bibliographic record

VenueOxford University Press eBooks · 2015
Typebook
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of GuelphUniversity of Ottawa
Fundersnot available
KeywordsSilenceCreativityAffect (linguistics)Modernism (music)PoliticsField (mathematics)AestheticsComposition (language)MusicalSociologyGender studiesArtPsychologyLiteratureSocial psychologyPolitical scienceLawCommunication

Abstract

fetched live from OpenAlex

This article examines the prejudices that women continue to experience in the field of composition in the twenty-first century. More specifically, it analyzes the host of factors that may be responsible for this reality from three perspectives: the notion that the language of modernist music is a gendered discourse, the role of precedent in the acceptance of women composers, and the role of societal stereotypes. The article looks at Catherine Parson Smith’s contention that the use of sexual linguistics has been detrimental to women artists during the modernist era; the various contexts that gave rise to the political positioning of the musical language of modernism; how stereotypes about artistic women affect the creativity and output as well as the professional behavior of women composers. Finally, it offers suggestions for overcoming the obstacles that prevent contemporary women composers from receiving due recognition.

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)0.005
metaresearch head score (Gemma)0.006
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: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.107

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0150.045
Scholarly communication0.0090.007
Open science0.0010.010
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0070.002

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 head0.050
GPT teacher head0.216
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; 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
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

Citations2
Published2015
Admission routes1
Has abstractyes

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