Cross-Linguistic Trends in Speech Errors: An Analysis of Sub-Lexical Errors in Cantonese
Bibliographic record
Abstract
Though past research on the sound structure of speech errors has contributed greatly to our understanding of phonological encoding, most of this research comes from a small set of majority languages with similar linguistic structures. To increase the linguistic diversity of relevant evidence, a large collection of speech errors was investigated in Cantonese, an under-studied language with unique phonological structures. In particular, the Cantonese data were examined for nine psycholinguistic effects commonly used as a lens on word-form encoding. Detailed quantitative analysis found that Cantonese has eight of these effects, providing broader cross-linguistic support for models based on these patterns. Yet Cantonese also exhibited differences with well-known Indo-European languages by having a higher rate of errors involving whole syllables and sub-constituents inside the syllable rime. These differences can be accounted for by recognizing the primacy of the syllable and mora in encoding Cantonese words, following proposals that have been made for Mandarin Chinese and Japanese.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".