Do Performance-Based Measures and Behavioral Ratings of Executive Functioning Complement Each Other in Predicting Reading and Mathematics in Chinese?
Bibliographic record
Abstract
We examined what executive functioning (EF) components predict reading and mathematics within the same study and whether the effects of behavioral ratings of EF overlap or complement those of performance-based measures. One hundred and nine Grade 2 Mandarin-speaking Chinese students from Chengdu, China (55 girls, 54 boys, Mage = 8.15 years), were assessed on measures of EF (planning, inhibition, shifting, and working memory), speed of processing, reading and mathematics. Parents also rated their children's EF skills using the Childhood Executive Functioning Inventory. Results of hierarchical regression analyses showed that only working memory among the performance-based EF measures predicted reading and mathematics. In addition, none of the behavioral ratings of EF made a significant contribution to reading and mathematics after controlling for mother's education and speed of processing. Taken together, these findings suggest that working memory is a domain general predictor of academic achievement, but only when measured with cognitive tasks.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".