Second language speakers’ awareness of their own comprehensibility
Bibliographic record
Abstract
Abstract This study investigated whether second language (L2) speakers are aware of and can manipulate aspects of their speech contributing to comprehensibility. Forty Mandarin speakers of L2 English performed two versions of the same oral task. Before the second task, half of the speakers were asked to make their speech as easy for the interlocutor to understand as possible, while the other half received no additional prompt. Speakers self-assessed comprehensibility after each task and were interviewed about how they improved their comprehensibility. Native-speaking listeners evaluated speaker performances for five dimensions, rating speech similarly across groups and tasks. Overall, participants did not become more comprehensible from task 1 to task 2, whether prompted or not, nor did speakers’ self-assessments become more in line with raters’, indicating speakers may not be aware of their own comprehensibility. However, speakers who did demonstrate greater improvement in comprehensibility received higher ratings of flow, and speakers’ self-ratings of comprehensibility were aligned with listeners’ assessments only in the second task. When discussing comprehensibility, speakers commented more on task content than linguistic dimensions. Results highlight the roles of task repetition and self-assessment in speakers’ awareness of comprehensibility.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".