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Record W4403707705 · doi:10.1123/shr.2023-0019

Building the Beach: Interest Convergence, (Black) Capitalism, and Air Jordans

2024· article· en· W4403707705 on OpenAlexaff
A. Lamont Williams, Amanda N. Schweinbenz, Ann Pegoraro

Bibliographic record

VenueSport History Review · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsUniversity of GuelphLaurentian University
Fundersnot available
KeywordsCapitalismConvergence (economics)EconomicsPolitical scienceEconomic growth

Abstract

fetched live from OpenAlex

This paper uses water waves as a metaphor to critically examine Black athlete activism and Derrick Bell’s interest-convergence principle as an analytical lens for understanding how Black Athletes leveraged the capitalist system of sport to build power through wealth. Specifically, we focus on how the convergence of interest between Michael Jordan, Nike, and the National Basketball Association “built the beach” on which the current wave of Black athlete activists stand. While Jordan has been noted for his lack of activism related to race-related issues in the United States, Jordan’s ability to accumulate billions of dollars in generational wealth through interest-convergence, he did lay the foundation for current Black athlete activists including Steph Curry and Lebron James. As such, Black athlete activists like James and Curry have the ability to speak up and speak out when they deem it is necessary without the fear of financial ruin or loss of livelihood.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0020.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.053
GPT teacher head0.315
Teacher spread0.263 · 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
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
Published2024
Admission routes1
Has abstractyes

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