Discrimination of L2 British English Monophthong Contrasts: The Case of L2 Thai Learners of English
Bibliographic record
Abstract
This article reports on the second language (L2) perception of contrasts among British English monophthongs. This study has two aims: 1) to explore the discriminability of contrasts in L2 British English monophthongs; and 2) to test the perceptual assimilation model-L2 (PAM-L2) towards the ability to discriminate British English contrasts. The contrasts considered were: /iː/-/ɪ/, /æ/-/ʌ/, /ɜː/-/ʌ/, /uː/-/ʊ/, /e/-/æ/, /ʌ/-/ɒ/, /ʊ/-/ɔː/, /ɑː/-/ʌ/, /ɜː/-/ɔː/. Fifty-two native speakers of Thai who were learning English as a foreign language in Thailand participated in a forced-choice ABX discrimination task. The participants were divided between two groups – those high-experienced and those low-experienced. The results evidence how both groups performed well on most contrasts (over 80% correct), except for /ʌ/-/ɒ/. Although the discriminability of the contrast /ʌ/-/ɒ/ was lower than with the other contrasts, the discrimination scores among both groups remained in a middle range (over 70%). No effect of L2 experience was found, thus suggesting that the abilities of both groups did not differ. The PAM-L2 was accurate in predicting that neither group of L2 Thai learners would have difficulty in discriminating the considered L2 sound contrasts. These results imply that the results gained from a perceptual assimilation task are useful in predicting the discriminability of L2 sound contrasts, as suggested by the PAM-L2.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".