Lexical encoding of L2 tones: The role of L1 stress, pitch accent and intonation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Native language prosodic structure is known to modulate the processing of non-native suprasegmental information. It has been shown that native speakers of French, a language without lexical stress, have difficulties storing non-native stress contrasts. We investigated whether the ability to store lexical tone (as in Mandarin Chinese) also depends on the first language (L1) prosodic structure and, if so, how. We tested participants from a stress language (German), a language without word stress (French), a language with restricted lexical tonal contrasts (Japanese), and Mandarin Chinese controls. Furthermore, German has a rich intonational structure, while French and Japanese dispose of fewer utterance-level pitch contrasts. The participants learnt associations between disyllabic non-words (4 tonal contrasts) and objects and indicated whether picture–word pairs matched with what they had learnt (complete match, segmental or tonal mismatch conditions). In the tonal mismatch condition, the Mandarin Chinese controls had the highest sensitivity, followed by the German participants. The French and Japanese participants showed no sensitivity towards these tonal contrasts. Utterance-level prosody is hence better able to predict success in second language (L2) tone learning than word prosody.
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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 | 0.002 | 0.000 |
| 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) | 0.004 | 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 it