Heritage language learners of English: Linguistic gaps and cognitive strengths
Bibliographic record
Abstract
PURPOSE: This study examined whether Heritage Language Learners (HLLs) of English display profile effects in their performance on knowledge- and processing-dependent measures relative to the standardised mean scores of monolingual speakers. The study also investigated the influence of several experiential factors on HLL performance. METHOD: Participants were 59 Arabic-speaking HLLs from six to nine years old. The children completed a battery of linguistic tests in their L1 and L2, as well as cognitive measures of short-term and working memory and non-verbal intelligence. RESULT: Significantly lower standardised scores were observed for HLLs as compared to the standardised mean scores on all Arabic/English language tasks except L2 word reading. HLLs scored at or above age-level expectations on cognitive measures except the Arabic nonword repetition task. Stepwise regression analyses examining variance in HLLs' performance, age and richness of environment consistently explained HLLs' performance in L1 Arabic, but different factors accounted for HLLs' performance in English depending on the task. Age was the only variable that consistently explained variance in performance on the cognitive measures. CONCLUSION: The results suggest that processing-dependent measures may be less sensitive to difference in language experience than traditional knowledge-based measures such as standardised measures of language and vocabulary.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".