Bibliographic record
Abstract
Despite the prevalence of bilingualism and rise in the popularity of bilingual education worldwide, few studies have explored struggling reader profiles in bilinguals. This dissertation is comprised of two three-year longitudinal studies that investigate profiles of struggling readers in the first (L1) and second (L2) languages of English(L1)-French(L2) bilinguals attending Canadian French immersion from grades 1 to 3. The first study investigated overlap and stability of L1 and L2 word reading profiles in a sample of 169 children. The difference in L1 and L2 overlap when word reading fluency in addition to accuracy skills are included in classification was also explored. Results indicated that classification based on accuracy and fluency captured bilingual reading difficulties (difficulties in both languages) more accurately. Across all grades, there was a significant relationship between being a struggling reader in L1 and in L2, with overlap ranging from 56% to 82%. Moreover, being a bilingual struggling reader in grade 1 was significantly related to being a bilingual struggling reader in grades 2 and 3. The second study investigated heterogeneity and stability of L1 and L2 word reading and reading comprehension profiles in a sample of 198 children. The role of first-grade measures of phonological awareness, rapid naming, and vocabulary in predicting profile membership was also examined. Results indicated that poor readers with low word reading and comprehension were the only struggling reader profile in all grades for French and in grades 1 and 2 for English. In grade 3, a second subgroup of poor comprehenders emerged in English. Status as an English struggling reader was highly stable from grades 1 to 3 whereas status as a French struggling reader became more stable in later grades. For French poor readers, phonological awareness predicted status across all grades whereas rapid naming and vocabulary only predicted status in grade 1. For English, rapid naming and phonological awareness predicted poor reader status in grade 1 and grade 2, respectively, while vocabulary predicted status as a poor reader and poor comprehender in grade 3. Overall, the findings have implications for our understanding of reading difficulties and for identification of struggling readers among bilinguals.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".