The impact of dual-language representation on reading development in bilingual children
Bibliographic record
Abstract
When reading, words are broken down into morphological components that involve meaning and letter-order information; These are activated in two languages in bilinguals. These dual representations facilitate reading in bilinguals proficient in both languages, with efficient cross-language activation and morphological processing. This dual activation may be detrimental in bilinguals with low-to-intermediate second-language (L2) proficiency, as overlap and interactions between strong- and weak-language representations of morphological components may inhibit lexical access. This study investigated how dual-language representation impacts access to morphological information during reading, and whether bilingual proficiency can mediate effects. Grade 6 monolingual (N = 26), high-proficient (HP; N = 11) and low-proficient (LP; N = 9) bilingual children participated. Standardized reading and L2 vocabulary measures were followed by a primed lexical decision task in English. Prime-target pairs were semantically related (e.g., walker-WALK), orthographically related (e.g., corner-CORN), or unrelated. Event related potential (ERP) and response time (RT) data were analyzed, focusing on how strength of dual language proficiency can impact reading in bilinguals. Comparisons between bilinguals and monolinguals were also examined. RTs and ERP N400 waveforms involving semantic processing were facilitated by semantically related primes for monolinguals. LP bilinguals performed like HP bilinguals, but with ERP N250 orthographic-prime facilitation. HP bilinguals relied more on orthographic information, with reduced primacy of semantic information in word identification. Results support interactive activation accounts of bilingualism, demonstrating that morpho-syntactic and -orthographic activation presents differently in bilinguals of varying L2 proficiency and in monolinguals; possibly driven by the quality of dual language representations.
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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 | 0.001 |
| 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 | 0.000 |
| 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".