Economic Freedom, Climate Culpability, and Physical Activity Indicators Among Children and Adolescents: Report Card Grades From the Global Matrix 4.0
Bibliographic record
Abstract
BACKGROUND: Macrolevel factors such as economic and climate factors can be associated with physical activity indicators. This study explored patterns and relationships between economic freedom, climate culpability, and Report Card grades on physical activity-related indicators among 57 countries/jurisdictions participating in the Global Matrix 4.0. METHODS: Participating countries/jurisdictions provided Report Card grades on 10 common indicators. Information on economic freedom and climatic factors were gathered from public data sources. Correlations between the key variables were provided by income groups (ie, low- and middle-income countries/jurisdictions and high-income countries/jurisdictions [HIC]). RESULTS: HIC were more economically neoliberal and more responsible for climate change than low- and middle-income countries. Annual temperature and precipitation were negatively correlated with behavioral/individual indicators in low- and middle-income countries but not in HIC. In HIC, correlations between climate culpability and behavioral/individual and economic indicators were more apparent. Overall, poorer grades were observed in highly culpable countries/jurisdictions in the highly free group, while in less/moderately free groups, less culpable countries/jurisdictions showed poorer grades than their counterparts in their respective group by economic freedom. CONCLUSIONS: Global-level physical activity promotion strategies should closely evaluate different areas that need interventions tailored by income groups, with careful considerations for inequities in the global political economy and climate change.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".