The polarizing politics of breaking’s inclusion in the Olympics
Bibliographic record
Abstract
As nations across the world incorporate breaking into their national sports systems in anticipation of the 2024 Paris Olympic Games, the new dancesport classification has resulted in a number of tensions and possibilities among dancers. The increased support from state governments and commercial sponsors in recent years has noticeably impacted dancers and their prospective careers. While this development has been promising in some countries, a range of disparities has become apparent across the globe, particularly in relation to funding, stipends, salaries, and contracts from state agencies or local DanceSport organizations. This disparity between the global north and south is becoming more pronounced in the lead-up to the Olympics. This creates a contradiction our issue explores: on the one hand, the Olympic opportunity is a sign that breaking is thriving around the world, while on the other, the resources required to participate successfully will, as ever, favour the globally powerful. Furthermore, the articles and statements in this issue seek to situate the Olympic project’s impacts on the dance, particularly in terms of colonizing mindsets, misogyny and unequal power relations more generally.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".