Bibliographic record
Abstract
Abstract This book provides a historical and cultural analysis of US–based dance-makers’ investment in intellectual property rights. Although federal copyright law in the United States did not recognize choreography as a protectable class prior to the 1976 Copyright Act, efforts to win copyright protection for dance began eight decades earlier. In a series of case studies stretching from the late nineteenth century to the early twenty-first, the book reconstructs those efforts and teases out their raced and gendered politics. Rather than chart a narrative of progress, the book shows how dancers working in a range of genres have embraced intellectual property rights as a means to both consolidate and contest racial and gendered power. A number of the artists featured in the book are well-known white figures in the history of American dance, including modern dancers Loïe Fuller, Hanya Holm, and Martha Graham, and ballet artists Agnes de Mille and George Balanchine. But the book also uncovers a host of marginalized figures—from the South Asian dancer Mohammed Ismail, to the African American pantomimist Johnny Hudgins, to the African American blues singer Alberta Hunter, to the white burlesque dancer Faith Dane—who were equally interested in positioning themselves as subjects rather than objects of property, as possessive individuals rather than exchangeable commodities. Choreographic copyright, the book argues, has been a site for the reinforcement of gendered white privilege as well as for challenges to it.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.068 | 0.004 |
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".