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Record W2111641223 · doi:10.4337/qmjip.2015.03.06

Tactical destabilization for economic justice: the first phase of the 1984–2004 rhythm & blues royalty reform movement

2015· article· en· W2111641223 on OpenAlexaff
Matt Stahl

Bibliographic record

VenueQueen Mary Journal of Intellectual Property · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsWestern University
Fundersnot available
KeywordsBluesRedressEconomic JusticeScholarshipLawSociologyPoliticsRacismPolitical scienceEconomicsManagement

Abstract

fetched live from OpenAlex

This article examines an early (1980s) phase in a two-decade effort towards ‘royalty reform’ in the US recording industry, whereby ageing African American rhythm & blues performers sought to redress systematic underpayment of record royalties. The record companies to which these performers had signed in the 1940s and 1950s had contractually obligated themselves to provide biannual statements of royalty accounts, and to pay artists record royalties, once initial costs had been recouped. Yet this is rarely how things turned out, and many performers found themselves broke and vulnerable as they reached retirement age, even though many of their records had remained in print and selling for decades. Drawing conceptually on critical race theory and social science scholarship, and empirically on primary source and archival documents, the article outlines the 1950s recording industry's ‘racialized political economy’ and offers an account of singer Ruth Brown and attorney Howell Begle's innovative and successful effort to narrate a counter-history of fraudulent accounting practices and casual racism that helped pressure companies like Warner Communications, MCA and Capitol/EMI into forgiving old production debts, renegotiating 40-year-old contracts, and paying millions of dollars in restitution through the formation and funding of the Rhythm & Blues Foundation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.085
GPT teacher head0.278
Teacher spread0.194 · 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 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

Citations1
Published2015
Admission routes1
Has abstractyes

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