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Record W2039849418 · doi:10.3138/md.43.1.100

From Jug Band to Dixieland: The Musical Development behind August Wilson's <i>Ma Rainey's Black Bottom</i>

2000· article· en· W2039849418 on OpenAlexvenueno aff
Susan C. W. Abbotson

Bibliographic record

VenueModern Drama · 2000
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsBluesWhite (mutation)Symbol (formal)StudioAmbivalenceMusicalLeveeArtAction (physics)HistoryChorusVisual artsLiteratureArt historyCartographyPsychologyPhilosophyPsychoanalysisGeography

Abstract

fetched live from OpenAlex

Ma Rainey takes place in 1927 and introduces us to a fictionalized version of the real blues singer Ma Rainey, spending an afternoon in the recording studio with her backup band. The latter dominates the action, and we watch as Cutler, Slow Drag, Toledo, and Levee practise a few tunes, chat about their lives, and squabble over their differences while being overseen by the people who have the real control: the white producer, Sturdyvant, and the band's agent, Irvin. Ma, with her entourage, appears late in the play to make her recording, and although she is a powerful symbol, it is Levee who dominates the action of the play and provides the shocking denouement in which he kills Toledo. One question the play raises is, Whom should we support — Ma or Levee? The play's title attests to the ambivalence of Wilson's answer. Although it names Ma, it actually refers to the song rather than to the person, and it is a song that Levee is trying to claim for his own. The similarities between the characters ensure that we cannot easily dismiss the claims of either one..

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0100.005
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.018
GPT teacher head0.202
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2000
Admission routes1
Has abstractyes

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