Cross-linguistic differences in prosodic cues to syntactic disambiguation in German and English
Bibliographic record
Abstract
This study examined whether late-learning English-German L2 learners and late-learning German-English L2 learners use prosodic cues to disambiguate temporarily ambiguous L1 and L2 sentences during speech production. Experiments 1a and 1b showed that English-German L2 learners and German-English L2 learners used a pitch rise and pitch accent to disambiguate prepositional phrase-attachment sentences in German. However, the same participants, as well as monolingual English speakers, only used pitch accent to disambiguate similar English sentences. Taken together, these results indicate the L2 learners used prosody to disambiguate sentences in both of their languages and did not fully transfer cues to disambiguation from their L1 to their L2. The results have implications for the acquisition of L2 prosody and the interaction between prosody and meaning in L2 production.
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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 | 0.001 |
| 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) | 0.000 | 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".