Vocabulary profiles and reading comprehension in young bilingual children
Bibliographic record
Abstract
Strong vocabulary knowledge is important for success in reading comprehension for English language learners (ELLs). The interplay between first (L1) and second language (L2) vocabulary knowledge in L2 English reading comprehension was examined to determine whether ELLs, whose command of L1 and L2 vocabulary varied across languages, differed in English reading comprehension in grades 2 and 4. ELLs (n = 105) were assigned to a bilingual profile group based on their L1 and L2 vocabulary knowledge and in relation to the sample: L1 dominant (strong L1), L2 dominant (strong L2), high balanced (strong in both), or low balanced (compromised in both). Relationships among L1 and L2 (English) vocabulary, nonverbal cognitive ability, word reading, and reading comprehension in English were examined. Results indicated that reading comprehension was related to bilingual profile, and that a three group model better characterized the sample when compared to the four group model that was initially hypothesized. L1 vocabulary was not uniquely predictive of L2 (English) reading comprehension. L2 vocabulary aligned betterwith reading comprehension concurrently in grade 2, and longitudinally in grade 4. In support of a common underlying cognitive processes perspective, individual differences in learning vocabulary may be a proxy forgeneral language learning ability, which supports reading comprehension.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".