Bibliographic record
Abstract
This research paper seeks to explore the intersection of race, seen through the predominantly Black athletic body of the NBA, with the rise of the capitalist, consumer-oriented entertainment industry of professional sports throughout the late 20th and early 21st centuries. It will attempt to illustrate how racial identity and capitalism have reacted to one another to create one of the biggest – and one of the most complicated – entertainment entities in North America. To explore this issue, I will outline the social setting from which Black athletes grew to participate in spectator sports, touching on notable persons such as Jackie Robinson, Muhammad Ali, and Bill Russell. I will then examine the importance of broadcasted sports and race’s role therein during the 20th century to contextualise capitalist practices in entertainment. I will conclude with an examination of capitalist practice as regulators for Black identity in the NBA by focusing on its direct and indirect attempts towards regulation. This will be done through an examination of Black athletes’ participation in social justice movements as measures of regulation, using the 1992 Rodney King trial riots and the events of Summer 2020 as comparative case studies. While this may appear to simply be an exploration of sports history, one should consider that sports are a primary form of entertainment in both North American and global popular culture. As such, this research project goes beyond an attempt to contribute to sports history, instead seeking to delve into the complementarity of social history, consumerism, and race.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".