Automaticity and Control: How Do Executive Functions and Reading Fluency Interact in Predicting Reading Comprehension?
Bibliographic record
Abstract
Abstract The authors investigated the roles of reading fluency in mediating and moderating the relation between executive functions and reading comprehension. Linguistically diverse students ( n = 106) were assessed on multiple measures of reading fluency at the passage and word levels, reading comprehension, and executive functions in grade 7 and again in grade 8. Path analyses using factor scores indicated that executive functions made indirect (mediated) contributions to reading comprehension via reading fluency, controlling for oral vocabulary and processing speed in both grades. Mediation was partial, with a statistically significant direct contribution of executive functions to reading comprehension remaining. Interaction analyses indicated that executive functions also statistically significantly interacted with reading fluency in predicting reading comprehension in both grades. Contrary to theoretical predictions, these interactions were positive, with executive functions predicting reading comprehension more strongly for students with higher reading fluency. Findings indicate that executive functions are implicated in reading fluency and that the contributions of executive functions and reading fluency to reading comprehension may be multiplicative rather than additive or compensatory.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".