Production quality of /r/ and /l/ liquids among Cantonese and Mandarin ESL learners
Bibliographic record
Abstract
Perceptual interference theories suggest L2 language learners produce phonemes based on their native language phonology (Iverson et al., 2004, Cognition). This present study investigated the impact of differing native language segmental inventories on the acquisition of the English r/l contrast. Northern Mandarin dialects exhibit coda /r/ similar to the English bunched /r/, while Cantonese exhibits no r-like liquids (Gick et al., 2003, under review). The Mandarin segmental inventory provides more of a basis for acquiring the English r/l contrast than the Cantonese inventory. It is therefore predicted that Mandarin speakers will acquire the r/l contrast with a lower level of experience with English than the Cantonese speakers. One Cantonese and two Mandarin ESL learners produced r/l sounds in minimally contrastive English words in simple and complex onset, coda, and intervocalic positions. The data were analyzed in two ways. Four native English listeners were asked to judge for each word whether the target consonant was /r/ or /l/. Also, ultrasound tongue images were analyzed for component /r/ and /l/ gestures. Results provided partial support for the hypothesis. Implications for theories of second language acquisition will be discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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; 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".