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Record W1826297421 · doi:10.21083/csieci.v1i2.16

“I wanted to live in that music:” Blues, Bessie Smith and Improvised Identities in Ann-Marie MacDonald’s <i>Fall on Your Knees</i>

2005· article· en· W1826297421 on OpenAlexaffvenue
Gillian Siddall

Bibliographic record

VenueCritical Studies in Improvisation / Études critiques en improvisation · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsLakehead University
Fundersnot available
KeywordsHoganBluesLyricsFemininityContext (archaeology)SingingSociologyPatriarchyGender studiesPower (physics)ArtLiteratureArt historyHistoryAnthropology

Abstract

fetched live from OpenAlex

This paper explores the link between the improvisatory nature of blues music and resistance to socially prescribed expectations for gender and sexuality in Ann-Marie MacDonald’s first novel, Fall on Your Knees (1996). When Kathleen Piper, one of the main characters in the novel, leaves her home in Cape Breton in1918 to pursue a classical singing career in New York, she finds herself transfixed, and subsequently transformed, by a performance by Jessie Hogan (a fictional character clearly modeled on Bessie Smith), in large part because of her remarkable improvised vocals. Hogan’s performance points to the rich history of the great blues women of this time period, women who, through their songs, costumes, and improvised lyrics and melodies, explicitly and implicitly tackled issues such as domestic violence and poverty, and challenged normative ideas of black female identity and sexual orientation. This history provides a critical context for Kathleen’s growing sense of autonomy and sexual identity, and this paper argues that the representation of Bessie Smith in the novel (in the guise of Hogan) enables possibilities for improvising new social relations and sexual identities.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
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 head0.072
GPT teacher head0.318
Teacher spread0.246 · 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.

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

Citations2
Published2005
Admission routes2
Has abstractyes

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