Morphological awareness and word reading in English language learners: Evidence from Spanish- and Chinese-speaking children
Bibliographic record
Abstract
ABSTRACT This study examined the effects of first language characteristics on the development of two aspects of English morphological awareness: derivational and compound awareness in English language learners (ELLs) with Chinese or Spanish as their first language. It also assessed the contribution of derivational and compound awareness to word reading in the two groups of ELLs as well as in monolingual English-speaking children. Participants included 89 Spanish-speaking ELLs, 77 Chinese-speaking ELLs, and 78 monolingual English-speaking children from Grade 4 and Grade 7. Results showed that Chinese-speaking ELLs performed similarly to monolingual English speakers on English compound awareness, and monolingual English speakers outperformed Spanish-speaking ELLs. Spanish-speaking ELLs and monolingual children, in contrast, both outperformed Chinese-speaking ELLs on derivational awareness. Another key finding was that in all three groups of children, morphological awareness made a unique contribution to word reading after controlling for nonverbal ability, maternal education, and other reading related variables. These results underscore the influence of first language structure on the development of second language morphological awareness, and the similar contribution of morphological awareness to word reading across monolinguals and ELLs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".