Cross-validation of the Canadian Assessment of Physical Literacy second edition (CAPL-2): The case of a Chinese population
Bibliographic record
Abstract
The present study aimed to culturally adapt the Canadian Assessment of Physical Literacy, Second Edition (CAPL-2) and examine its validity and reliability among Chinese children aged 8 to 12 years. The original manual of CAPL-2 was translated and culturally adapted from English into Chinese. A total of 327 children (153 boys, mean age = 10.0) completed CAPL-2 (Chinese) assessments. Internal consistency reliability and construct validity for subscales and total model was explored. Results reported a good fit after adjusting for covariation paths, chi-square (χ2 = 70.16, df = 43, p < 0.05), RMSEA = 0.04, 90% CI (0.024 – 0.062), CFI = 0.94, TLI = 0.90. Motivation and Confidence showed a good internal consistency (α = 0.82), compared to Knowledge and Understanding (α = 0.52). In general, there were few significant correlations between age and the subdomains as developmentally expected, and gender differences were observed with boys performing better than girls in total CAPL2 (Chinese) scores. This study was the first to cross-validate the CAPL-2 into the Chinese population. CAPL-2 (Chinese) offers the possibility of assessing physical literacy for researchers and practitioners and Chinese children’s physical literacy development could be easily tracked in school settings.
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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 | 0.020 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".