#REDS (Relative Energy Deficiency in Sport): time for a revolution in sports culture and systems to improve athlete health and performance
Bibliographic record
Abstract
Changing a sport system requires the appointment of new leaders or a grass roots cultural revolution. ‘I got caught in a system designed by and for men, which destroys the bodies of young girls,’ said Mary Cain as she cast light on her toxic coach/athlete relationship and exposed unhealthy coaching and nutrition practices. Her candour has inspired a social media movement calling for changes to women’s sport.1 In the following days, major news publications followed up with similar reports of athletic women being body shamed.2–4 It is time for a drastic paradigm change in women’s sport, coupled with education at all levels to improve the long-term health and athletic achievement of female athletes. The shift needs to include: 1. Raising awareness of the negative effects of chronic low energy availability (LEA) (calorie restriction) so athletes can make wise choices for their own long-term health. 2. Updating and developing best-practice protocols and safe standards for monitoring body composition/weight. 3. Eliminating toxic training environments featuring abusive body shaming. Overexercising or underfueling, occurring consciously or subconsciously, can cause Relative …
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.018 | 0.018 |
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".