Learning to read in English as third language
Bibliographic record
Abstract
The present study compared the influence of bi-literate bilingualism versus mono-literate bilingualism on the development of literary skills in English as L3. Two main predictions were made. First, it was predicted that Russian (L1) literacy would benefit decoding and spelling acquisition in English (L3), that is, bi-literate bilingualism would be superior to mono-literate bilingualism. Second, it was hypothesized that there would be positive transfer of phonological processing skills from L1 Russian to L3 English even in the context of two linguistically and orthographically distinct languages. The sample of 107 11-year-old children from Haifa, Israel, were divided into three groups matched in age, gender, social-economic level, verbal and non-verbal IQ: bi-literate bilinguals, mono-literate bilinguals and mono-literate monolinguals. The research was conducted in two stages. In the first stage a wide range of linguistic, meta-linguistic, cognitive and literacy tasks in Hebrew (L2) and in Russian (L1) were administered. In the second stage linguistic, meta-linguistic and literacy skills in English (L3) were assessed. The results demonstrated that bi-literate bilinguals outperformed mono-literate bilingual and mono-lingual children on a number of basic literacy measures (phoneme deletion and analysis, pseudoword decoding and spelling) in English (L3). Even after controlling for (L2) Hebrew reading accuracy, bi-literacy independently explained 16% of the variance in English reading accuracy among Russian-Hebrew fifth grade 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 0.001 |
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".