Community size and sport participation across 22 countries
Bibliographic record
Abstract
The objective of this study was to examine, across 22 countries, the association between community size and individual sport, team sport, and exercise participation. Hierarchal non-linear Bernoulli modeling is used to examine the association between community size (100,000-10,000; <10,000) and (a) individual sport, (b) team sport, and (c) exercise participation. After controlling for country-level clustering and demographic variables, those residing a community with between 100,000 and 10,000 residents are more likely to participate in individual sport [odds ratio (OR) = 1.14; 95% confidence interval (CI) = 1.05-1.23] while residing in a community with less than 10,000 residents is unrelated (OR = 1.06; 95% CI = 0.96-1.19). Those residing in communities with between 100,000 and 10,000 residents were more likely to participate in team sport (OR = 1.21; 95% CI = 1.01-1.45) while residing in a community with less than 10,000 residents is unrelated (OR = 1.02; 95% CI = 0.88-1.18). Residing in a community with between 100,000 and 10,000 residents is unrelated to exercise participation (OR = 0.97; 95% CI = 0.89-1.7), while residing in a community with less than 10,000 residents is negatively related to exercise participation (OR = 0.86; 95% CI = 0.79-0.93). These findings provide novel evidence that communities between 100,000 and 10,000 residents are related to increased sport participation, particularly team sport participation.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".