First- and final-semester non-native students in an English-medium university: Judgments of their speech by university peers
Bibliographic record
Abstract
By the end of their studies, non-native speakers of English studying at English-medium universities have had several years of exposure to English in that setting. Do non-native students, particularly those enrolled in non-languagerelated programs, show different levels of second language (L2) speaking ability in their final semester of studies than non-native students in their first semester, as judged by other students in the university community? In this exploratory cross-sectional study, two matched groups of L2 English university students in their first or final semester of study in non-language-related programs ( N = 20) were recorded in mock job interviews. The students were rated by two groups of raters for accentedness, comprehensibility, fluency, and communicative effectiveness. Both rater groups were university students; one group was from diverse academic programs, while the other group was studying human resource management (HRM). Although the first- and final-semester L2 English students differed in how long they had studied in English, no significant difference in ratings between first- and final-semester students was found. However, the two rater groups differed in how they rated accentedness and comprehensibility, suggesting that the nature of listeners' previous academic experience (e.g., with HRM) influences their judgments. The use of holistic rating scales to evaluate L2 speech is discussed, as well as the relationship between the nature of language exposure and the performance of the student and rater groups.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| 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".