Contextual effects on the perception of English interdental fricatives /<b> <i>θ</i> </b>/ by Mandarin Chinese learners
Bibliographic record
Abstract
Mandarin Chinese lacks interdental fricatives, causing Mandarin Chinese learners to struggle in perceiving these sounds in English. This study explores how word position and vocalic context affect the perception of /θ/ by Mandarin Chinese learners. We test Mandarin Chinese learners and native English speakers using words containing the sound /θ/ in word-initial, medial, and final positions (e.g., think, nothing, bath), and in different vocalic contexts (e.g., think vs. thank). Participants complete a forced-choice identification task, listening to the target words in a carrier sentence and selecting the perceived word from two options. We record reaction time and accuracy for statistical analysis. We use generalized additive mixed modeling for reaction time and logistic mixed-effects modeling for response accuracy. We expect Mandarin Chinese learners to show longer reaction times and higher accuracy in perceiving /θ/ in the word-initial position than in media and final positions. Additionally, we anticipate that Mandarin Chinese learners perceive /θ/ more accurately when it is followed or preceded by a high vowel rather than a non-high vowel. Understanding these perception differences and contextual influences can inform more effective language teaching strategies, particularly in pronunciation training for Mandarin Chinese learners.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".