Bibliographic record
Abstract
Attempts to account for consonant cluster acquisition are always made into two aspects. One is transfer of the first language (L1), and another is markedness effects on the developmental processes in second language acquisition. This study has continued these attempts by finding out how well Thai university students were able to perceive English onset and coda clusters when they were second year and fourth year students. This paper also aims to investigate Thai speakers’ opinions about their listening and speaking skills, and whether their course subjects enhanced their performance. To fulfil the first objective, a pretest and posttest were launched to measure how the 34 Thai participants were able to identify 40 onset and 120 coda clusters at different periods of time. The statistical findings show that even though their overall scores in the fourth year were higher than those in the second year, there was no statistically significant difference in both major types of clusters [t = -1.29; p value >0.05 in onsets; t = -0.28; p value >0.05 in codas]. The Thai participants performed slightly better in onset (84% / 86%) than in coda (70% / 71%). To complete the second objective of the study, a 24-item questionnaire was distributed to the participants. The responses indicated positive opinions about their listening and speaking skills and the English courses they took in a four-year study. However, they still had difficulty identifying some English consonant clusters even though those were widely used or found. Finally, most participants claimed that English Phonetics and Phonology Course was one of the significant course subjects instrumental in establishing their fundamental knowledge of how to pronounce English words and develop their listening skill as well.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".