Bibliographic record
Abstract
The high prevalence of workplace sexual misconduct in Euro-American dance has been widely known yet seldom discussed throughout its history. However, in recent years, whistleblowers have disclosed misconduct at multiple large dance institutions across the United States, Britain, and Canada. These reported cases have inspired a considerable reckoning in the dance community, their acknowledgment having been met with increasing calls for institutional change.In ‘The Sad Choreography of Systemic Misconduct, From Cause to Affect’, author Martin Austin examines how misconduct occurs at seven Euro-American dance institutions as a choreography repeated with devastating similarity across cases. This critical essay does so by finding commonalities between whistleblower testimonies reported to print media platforms between 2018 and 2022. The institutionalized differentials in power, relational proximity between bodies, and sequence of violence conducted by perpetrators together amount to a choreography that systemizes how dancers experience misconduct by their superiors.For Baruch de Spinoza, sadness is the root of all negative affects including hatred, fear, and more. Sadness is theorized as being caused by a lack of agency: much like what is experienced by survivors in these acts of misconduct. Yet because this sadness is shared by past victims and allies alike, it may also be capable of uniting dancers to demand accountability and change in dance's institutionalized practices. If sadness is caused by misconduct, that sadness can also be affectively witnessed, felt, and compounded by others when survivors choose to share their experiences. This article therefore aims to catalogue the stories of survivors, and harness the sadness felt by readers to bolster a collective movement to change Euro-American dance institutions' problematic practices.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.014 | 0.041 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".