Does a Speaking Task Affect Second Language Comprehensibility?
Bibliographic record
Abstract
The current study investigated task effects on listener perception of second language (L2) comprehensibility (ease of understanding). Sixty university‐level adult speakers of English from 4 first language (L1) backgrounds (Chinese, Romance, Hindi, Farsi), with 15 speakers per group, were recorded performing 2 tasks (IELTS long‐turn speaking task and TOEFL iBT integrated listening/reading and speaking task). The speakers' audio recordings were evaluated using continuous sliding scales by 10 native English listeners for comprehensibility as well as for 10 linguistic variables drawn from the domains of pronunciation, fluency, lexis, grammar, and discourse. In the IELTS task, comprehensibility was associated solely with pronunciation and fluency categories (specifically, segmentals, word stress, rhythm, and speech rate), with the Farsi group being the only exception. However, in the cognitively more demanding TOEFL iBT integrated task, in addition to pronunciation and fluency variables, comprehensibility was also linked to several categories at the level of grammar, lexicon, and discourse for all groups. In both tasks, the relative strength of obtained associations also varied as a function of the speakers' L1. Results overall suggest that both task and speakers' L1 play important roles in determining ease of understanding for the listener, with implications for pronunciation teaching in mixed L1 classrooms and for operationalizing the construct of comprehensibility in assessments.
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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 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".