Flexibility in young second‐language learners: examining the language specificity of orthographic processing
Bibliographic record
Abstract
This study examines whether orthographic processing transfers across languages to reading when the writing systems under acquisition are sufficiently related. We conducted a study with 76 7‐year‐old English‐first‐language children in French immersion. Measures of English and French orthographic processing (orthographic choice tasks) and standardised measures of English and French word reading (Woodcock and FIAT) were taken, in addition to verbal and nonverbal ability, and phonological and morphological awareness. Analyses reveal significant contributions of orthographic processing to reading both within and across the two languages, despite the inclusion of control variables. Findings of the transfer of orthographic processing skills to reading across languages suggest that orthographic processing may not be as language specific as previously hypothesised. We discuss the several similarities between English and French, such as a shared alphabet and cognates, that may drive transfer across languages in the context of current theories of second‐language reading development.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".