Predictors of word decoding and reading fluency across languages varying in orthographic consistency.
Bibliographic record
Abstract
Very few studies have directly compared reading acquisition across different orthographies. The authors examined the concurrent and longitudinal predictors of word decoding and reading fluency in children learning to read in an orthographically inconsistent language (English) and in an orthographically consistent language (Greek). One hundred ten English-speaking children and 70 Greek-speaking children attending Grade 1 were examined in measures of phonological awareness, phonological memory, rapid naming speed, orthographic processing, word decoding, and reading fluency. The same children were reassessed on word decoding and reading fluency measures when they were in Grade 2. The results of structural equation modeling indicated that both phonological and orthographic processing contributed uniquely to reading ability in Grades 1 and 2. However, the importance of these predictors was different in the two languages, particularly with respect to their effect on word decoding. The authors argue that the orthography that children are learning to read is an important factor that needs to be taken into account when models of reading development are being generalized across languages.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".