How English-Cantonese Bilinguals Use Stress and Statistical Cues for Word Segmentation
Bibliographic record
Abstract
This study investigates how English-Cantonese bilingual and English monolingual adults use stress-based and statistical cues to segment words in continuous speech. Participants will be exposed to two artificial languages: one based on English-like syllables and one based on Cantonese-like syllables. By presenting speech streams containing conflicting stress and statistical cues, we aim to determine whether bilinguals develop distinct cue-weighting strategies for each language or adopt a unified approach that integrates cues from both. Additionally, we will examine how bilinguals’ performance compares to their monolingual peers. Real-time pupillary responses measured via eye-tracking will provide insights into cue engagement and segmentation strategies, shedding light on cognitive differences between monolingual and bilingual adults.
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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 | 0.005 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.033 | 0.005 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 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; both teacher heads agree on what is shown here.
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".