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Record W3180330878 · doi:10.1017/9781108773522.015

“Defeat Measured in the Jumping Cadences of Triumph”

2021· book-chapter· en· W3180330878 on OpenAlexaff
Tim A. Ryan

Bibliographic record

VenueCambridge University Press eBooks · 2021
Typebook-chapter
Languageen
FieldArts and Humanities
TopicCultural History and Identity Formation
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBluesJazzWrightTheme (computing)Harlem RenaissanceArtArt historyLiteratureMusicHEROMainstreamScholarshipHistoryPerformance artVisual artsMusic educationPhilosophyTheology

Abstract

fetched live from OpenAlex

Richard Wright’s relationship with African American music was fundamentally paradoxical: he was both thoroughly immersed in and profoundly detached from such genres as blues and jazz. While he listened to black music avidly, its presence in his fiction is minimal, and—like other progressives and literary figures of his time—he tended to see blues and jazz not as art in themselves, but as vital and raw folk material out of which the literati might create art. What is more, Wright emerged as an author and produced his most canonical works during a relative hiatus in blues history, as well as at a moment when jazz existed primarily as mainstream entertainment in the form of big-band swing. Although critics conventionally focus upon the few fleeting references to African American music in Wright’s fiction, revisionist scholarship might bring the music to bear on the author’s work instead. There are, for example striking parallels of topic, theme, language, and imagery between Wright’s “Down by the Riverside” (1938) and Charley Patton’s song about southern flooding, “High Water Everywhere” (1930). Critics, then, can fill in the blues and jazz gaps in Wright’s work that he was unable to complete himself.

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 score0.009
Threshold uncertainty score0.029

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 studies0.0070.013
Scholarly communication0.0040.004
Open science0.0010.003
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0090.002

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 head0.065
GPT teacher head0.191
Teacher spread0.125 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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Same venueCambridge University Press eBooksSame topicCultural History and Identity FormationFrench-language works237,207