First‐Language Longitudinal Predictors of Second‐Language Literacy in Young L2 Learners
Bibliographic record
Abstract
Abstract Can young students’ early reading abilities in their first language (L1) predict later literacy development in a second language (L2)? The cross‐language relationships between Chinese (L1) and English (L2) among 87 Hong Kong students were explored in a longitudinal study. Chinese word‐reading fluency, Chinese rapid digit naming, and Chinese rhyme awareness at age 7 (grade 1), with age and IQ taken into account, were significant concurrent and longitudinal predictors of English word reading, and text‐level reading and writing skills across ages 7–10. These three Chinese measures together accounted for 16–28% of unique variance in the English literacy tasks across the three‐year period. Students who showed word‐reading difficulties in Chinese in grade 1 also performed more poorly than average Chinese readers in English reading and related cognitive tasks later on, especially on phonological tasks. The results provided evidence for the cross‐language transfer of cognitive‐linguistic abilities between two distinctly different orthographies. L1 markers underlying reading difficulties in both L1 and L2 can serve as early indicators of possible reading problems that may arise later in L2. These findings have clinical, educational, and theoretical implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".