The Predictive Effects of L1 and L2 Early Literacy Indicators on Reading in French Immersion
Bibliographic record
Abstract
Abstract: This study explored the predictive effects of within- and cross-language early literacy indicators with regard to second language (L2) reading achievement in a Grade 3 entry-point French immersion (FI) program. Kindergarten students (N = 83) in a regular English program were administered English early literacy measures. Three years later, once students entered the FI, 56 students from the original cohort were reassessed using French literacy measures. This allowed for an examination of the long-term connections between first language (L1) early literacy indicators and L2 reading outcomes. Regression analysis revealed that L1 early literacy skills relating to aspects of phonological awareness and, more importantly, alphabetic knowledge were significant predictors of L2 reading even when school-based L2 learning was delayed several years. With respect to the French literacy indicators, knowledge of the alphabet and related measures were again significant predictors of L2 reading performance. The predictive effects of French indicators were significant even in the first few months of FI. These results provide additional information about the predictive effects of within- and cross-language early literacy indicators and the extent to which they can be used to identify students who may be at risk for reading difficulties in their L2.
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".