The role of stress processing abilities in the development of bilingual reading
Bibliographic record
Abstract
French and Dutch differ regarding the manifestations and lexical functions of the stress pattern of words. The present study examined group differences in stress processing abilities between French‐native and Dutch‐native listeners, thus extending previous cross‐linguistic comparisons involving Spanish‐native and French‐native adults. The results show that Dutch‐native first‐graders significantly outperformed French monolinguals, and that French‐native listeners schooled in Dutch produced intermediary performances, suggesting that stress‐processing abilities are a learnable set of skills. The present study also examined the contribution of stress processing abilities to reading development in Dutch, a stress‐based language, compared to that in French, a syllable‐based language. Although the expected correlation between stress processing abilities and reading was not observed in the Dutch monolinguals, such correlation was observed in the French‐native bilinguals schooled in Dutch and not in the Dutch‐native bilinguals schooled in French. This suggests that stress processing abilities influence reading development in a second, stress‐based, language. Moreover, the monolinguals and bilinguals schooled in Dutch showed significant associations between lexical development and stress processing abilities. Ways in which prosody might be involved in lexical and reading development are explored and discussed.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".