Bibliographic record
Abstract
Childhood obesity has become a concern in most economically developing countries.The reasons for this seem clear: too much food and not enough movement.This dilemma has received much scientific attention, and the question arises: How do we get children to move and make good eating choices?In their opinion piece in this issue of the Journal of Sport and Health Science (JSHS), Ring-Dimitriou et al. 1 make an argument that playing structured and supervised traditional sports will help children with obesity, where traditional sports include football, handball, gymnastics, athletics, and martial arts.Khan et al., 2 in a widely cited The Lancet paper, argue what seems obvious: that sport and physical activity may contribute to the overall health of nations.In a previous issue of JSHS, Castagna et al. 3 make an appeal to the value of team sports, reminding us how much fun it was to play soccer, basketball, handball, and rugby when we were children.JSHS has even devoted a special topic of traditional sports and their impact on physical well-being.4À9 However, questions remain.For example, why, during the time we were growing up (in my case >50 years ago), were there so few children with obesity?Why were sport and exercise and games so much fun then, and why did they occupy so much of our spare time?Why, for many of today's children, does moving and playing team sports and running around in parks and forests seem such a chore?Of course, the world today is different from what it was 50 years ago, and children grow up in different circumstances now compared with then.But what are the conceptual differences between now and then?What did I have as a 5-or 8-or 12-year-old boy that children now typically do not have anymore?There are several answers.Opportunity-When I stepped outside my home, I had access to a hilly grass field, a playground, a park, a huge forest, and a soccer and team handball field within minutes of walking.In the winter, the grassy hill became my downhill skiing slope, the handball field my skating rink.During summer vacation, when we were allowed to stay out until dark, the street became a volleyball court, a soccer pitch, or a place to play hide and seek with kids ranging from 3 to 15, boys and girls.Everybody was outside; being inside was boring, and parents did not want their children inside, definitely not on a warm, sunny, summer afternoon or early evening.The playground started right outside the house.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.015 | 0.020 |
| Insufficient payload (model declined to judge) | 0.060 | 0.049 |
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".