Simple rules for creating and sustaining an anti-racist sport and exercise organisation
Bibliographic record
Abstract
The murders of George Floyd and Breonna Taylor in 2020 incited a surge in anti-racism efforts across the world. Many organisations, including those related to sport and exercise, were quick to make their stances on racism clear. In efforts to promote anti-racism, new equality, equity, diversity and inclusion (EEDI) groups were formed, while many individuals joined protests and took to social media to raise awareness of the racism that Black, Indigenous, and People of Colour (BIPOC) continue to face. In the sporting world, a significant burden and labour is placed on athletes of colour to make strides towards anti-racism. However, every individual working with the sport and exercise industry must act to condemn and eradicate racism. Sport and exercise organisations are in an opportune place to contribute to anti-racism efforts because of their wide reach, yet there is a need for clear advice on how to make change. Therefore, we provide commentary on 10 simple ‘rules’ that can support sport and exercise organisations in creating and sustaining an anti-racism.
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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 | 0.063 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.062 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.010 | 0.013 |
| Research integrity | 0.080 | 0.082 |
| Insufficient payload (model declined to judge) | 0.006 | 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".