Bibliographic record
Abstract
This dissertation explores the early identification and classification of struggling readers in English-French bilingual children enrolled in Canadian French immersion programs. It is comprised of two studies that investigate profiles of struggling readers in the first (L1) and second (L2) languages of English-French bilinguals attending Canadian French immersion in grades 1 and 3. The first study examines the effectiveness of a Dynamic Screening of Phonological Awareness (DSPA) in identifying at-risk readers among Grade 1 English-French bilingual children in a Canadian French immersion context. Specifically, we investigated whether DSPA measures administered in English (L1) and French (L2) predicted French word reading outcomes and at-risk status beyond static phonological awareness measures. Ninety-eight students were assessed using a battery of standardized and dynamic literacy measures in both languages. Logistic and hierarchical regression analyses revealed that while the French DSPA did not significantly enhance within-language prediction beyond static measures, the English DSPA significantly improved cross-language prediction of reading difficulties in French. Inclusion of the English DSPA in the model increased sensitivity and specificity, suggesting that dynamic assessment in L1 can effectively support early identification of at-risk readers in L2. Study 2 investigates reading profiles in English-French bilingual children enrolled in French immersion programs, focusing on the identification of struggling reader profiles using both the cut-off method and Latent Profile Analysis (LPA). Participants were 247 students in Grade 3, and their reading profiles were classified as good/average readers, poor decoders, and poor comprehenders. The findings reveal moderate to strong overlap between the two methods in identifying good/average readers and poor decoders in both languages, but inconsistencies in identifying poor comprehenders, particularly in French. Regression analyses revealed that phonological awareness was a significant predictor of poor decoder status across both methods and languages, while vocabulary predicted poor comprehender status in English but not in French. These findings underscore the role of phonological awareness and vocabulary in bilingual reading development and highlight the importance of employing multiple identification methods to support struggling readers effectively.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".