Bibliographic record
Abstract
Purpose We investigated the cognitive and linguistic processes that underlie reading in Arabic in relation to a well-defined theoretical framework of reading and the factors that underlie reading. Method The sample was 201 (101 boys, 100 girls) 3rd-grade Arabic-speaking children. Children were administered measures of Vocabulary, Phonological Awareness (PA), Naming Speed, Orthographic Processing, Morphological Awareness (MA), Memory, Nonverbal Ability, and 5 reading outcomes. Hierarchical regression analyses were conducted for each of the 5 reading outcomes to investigate the predictors of children's reading. Results Each of the constructs explained unique variance when added to the model. In the final models, PA was the strongest predictor of all outcomes, followed by MA. In a follow-up analysis, participants were divided into good and poor decoders, based on their Pseudoword Reading scores. Good decoders outscored poor decoders on every measure. Within-group regression analyses indicated that poor decoders relied on more component processes than good decoders, suggesting a lack of automaticity. Variance in reading outcomes was better predicted for poor decoders than for good decoders. Conclusion These results indicate that standard predictors apply well to Arabic, showing the particular importance of PA and MA. Longitudinal and instructional studies are required to determine developmental patterns and ways to improve reading performance.
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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 | 0.027 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".