The development of orthographic processing skills in children in early French immersion programs
Bibliographic record
Abstract
Children learning to read in two languages are faced with orthographic features from both languages, either unique to a language or similar across languages. In the present study, we examined how children develop orthographic processing skills over time (from grade 1 to grade 2) with a sample of Canadian children attending a French immersion program and we investigated the underlying factor structure of orthographic skills across English and French. Two orthographic processing tasks were administered in both languages: lexical orthographic processing (e.g. choose the correct spelling from people–peeple) and sub-lexical orthographic processing (e.g. which is the more word-like vaid–vayd?), which included both language-specific and language-shared orthographic regularities. Children’s performances in sub-lexical tasks increased with grade but were comparable across languages. Further, evidence for a one factor model including all measures suggested that there is a common underlying orthographic processing skill that cuts across measurement and language variables. Keywords: orthographic processing; reading; French immersion; bilinguals; second language learners
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".