Robin Hood in SEM? What can we take from elite sport to give back to wider public health?
Bibliographic record
Abstract
The current pandemic has made abundantly clear that when we are forced to reduce life to the essentials, physical activity becomes more important than ever. Even before COVID-19 frustrated our daily routines, the concept of physical activity for health already had begun to enjoy a surge in popularity. When the WHO released its 2020 Guidelines on Physical Activity and Sedentary Behaviour this past November,1 2 the message was simple—every move counts. That is, moving for even a few minutes a day is better than nothing and has proven health benefits. At the other end of the exercise spectrum, there is an increasing interest in how regular intense exercise, similar to that undertaken by elite sportspersons, may benefit health and longevity.3 Is there a role for elite sport to inform public health interventions? Influential international organizations think so, including the WHO, who renewed their partnership with a new work plan under a Memorandum of Understanding with the International Olympic Committee (IOC) last May.4 As IOC President Thomas Bach reminded us, ‘sport can save lives’ and is about more than competition. While Bach’s words may resonate with our readership, they also challenge the established notion that sport—elite sport in particular—is somehow set …
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".