The /s/-/ʃ/ confusion by Japanese ESL learners in grapheme-phoneme correspondence: bias towards [s] and
Bibliographic record
Abstract
It is generally believed that Japanese English-as-a-second-language (ESL) learners tend to pronounce English /si j , sɪ/ as [ʃi j , ʃɪ], such as see and sip as she and ship respectively, and these errors are typically attributed to the Japanese phonotactic constraint *[si(:)]. However, Nogita (2010) reveals that such errors are due to their misinterpretation of the spellings of and , not due to articulatory and perceptual difficulties. In this present study, I further reinforced Nogita’s (2010) argument by conducting a reading task in which 42 Japanese ESL learners read nonsense words containing and , and a spelling task in which they spelled nonsense words containing [s] and [ʃ]. In the reading task, I found their strong tendency of mispronouncing as [s], presumably because they assumed that [s] sounded more English-like. In the spelling task, they misspelled [ʃ] as more frequently than [s] as , presumably due to kunrei-shiki Japanese romanization interference. Moreover, by 29 of them, their grapheme-to-phoneme conversion and phoneme-to-grapheme conversion patterns were not consistent, indicating that they had not acquired the English GPC rules, -/s/ and -/ʃ/.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".