Learning to assign stress in a second language: The role of second-language vocabulary size and transfer from the native language in second-language readers of Italian
Bibliographic record
Abstract
Abstract Learning to pronounce a written word implies assigning a stress pattern to that word. This task can present a challenge for speakers of languages like Italian, in which stress information must often be computed from distributional properties of the language, especially for individuals learning Italian as a second language (L2). Here, we aimed to characterize the processes underlying the development of stress assignment in native English and native Chinese speakers learning L2 Italian. Both types of bilinguals produced evidence supporting a role of vocabulary size in modulating the type of distributional information used in stress assignment, with an early bias for Italian's dominant stress pattern being gradually replaced by use of associations between orthographic sequences and stress patterns in more advanced bilinguals. We also obtained some evidence for a transfer of stress assignment habits from the bilinguals’ native language to Italian, although only in English native speakers.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".