Age of exposure to L2 and literacy moderates within- and cross-language support for L2 reading: Insights from French readers in Côte d’Ivoire
Bibliographic record
Abstract
Background: Globally, many children learn to read exclusively in a second language (L2), but this phenomenon is understudied. Literacy typically reciprocally builds on pre-existing language knowledge, with phonological awareness (PA) and vocabulary predicting literacy outcomes, and themselves being impacted by literacy. Bilinguals demonstrate cross-linguistic transfer (CLT) of language skills from their first language (L1) to reading in L2, but this research primarily covers contexts where children have at least some level of L1 literacy. This raises the question of whether CLT occurs in the absence of L1 literacy, or if the reciprocal relation between language and literacy must be built in L1 before it can transfer to L2 reading.Methods: The current study examines the role of L1 and L2 language skills in supporting literacy in emergent readers in rural Côte d’Ivoire (n=1969, females = 912), between first and fifth grade. The broad range of ages both within and across grades allows us to examine how the age of first exposure to literacy impacts this relation. Results: We found robust evidence of CLT of PA, which was not moderated by age of literacy acquisition. Within-language support of literacy was moderated by age of literacy exposure, with literacy more strongly predicted by vocabulary and less by PA for those who started school on time. This effect fully mediated the detrimental impact of a late school start. Conclusions: These results have implications for theories of CLT and sensitive periods for literacy acquisition, and for efforts to reduce illiteracy in the Global South.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".