Compounds, competition, and incremental word identification in spoken Cantonese
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
The majority of words in Cantonese are compounds, which seems likely to burden the process of identifying words in running speech. Cantonese is also a stress-timed language, which reduces the potential for durational contrasts to distinguish embedded constituents from self-standing words. The current study demonstrates the challenge of identifying words in spoken Cantonese. As a compound unfolds, listeners are more likely to consider an onset-embedded constituent as the intended word than the actual word they are hearing – a result that seems poorly adapted to the prevalence of compounds. However, the results also show these challenges are offset by sentence-based cues, such as those provided by noun classifiers. This occurs despite variability in classifier-noun pairings and the fact that adult speakers often show incomplete mastery of these pairings. Together the results demonstrate how even highly biased cases of lexical competition are overcome by sentence-level constraints that may be only moderate in strength.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 |
| 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 it