Democracy predicts sport and recreation membership: Insights from 52 countries
Bibliographic record
Abstract
Although evidence suggests sport and recreation are powerful contributors to worldwide public health, sizable gender differences persist.It is unknown whether country characteristics moderate gender differences across countries.The primary purpose of this study was to examine if countries' levels of democracy and/or gender inequality moderate gender differences in sport and recreation membership across countries.The secondary purpose was to examine if democracy and/ or gender inequality predicts overall rates of sport and recreation membership for both males and females.This study involved a nested cross-sectional design and employed the sixth wave (2013) of the world value survey (n Ss = 71,901, n countries = 52).Multiple hierarchal nonlinear Bernoulli models tested: (1) if countries' levels of democracy moderate gender differences in sport and recreation membership; and (2) if democracy is associated with increased sport and recreation membership for both males and females.Countries' level of democracy fully moderated gender differences in sport and recreation membership across countries.Moreover, democracy was positively associated with both male and female membership, even when controlling for individual and country-level covariates.Democratic political regimes may confer health benefits via increased levels of sport and recreation membership, especially for females.Future research should test mediating mechanisms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".