Second Language Comprehensibility Revisited: Investigating the Effects of Learner Background
Bibliographic record
Abstract
The current study investigated first language (L1) effects on listener judgment of comprehensibility and accentedness in second language (L2) speech. The participants were 45 university‐level adult speakers of English from three L1 backgrounds (Chinese, Hindi, Farsi), performing a picture narrative task. Ten native English listeners used continuous sliding scales to evaluate the speakers' audio recordings for comprehensibility and accentedness as well as 10 linguistic variables drawn from the domains of pronunciation, fluency, lexis, grammar, and discourse. Comprehensibility was associated with several linguistic variables (segmentals, prosody, fluency, lexis, grammar), but accentedness was primarily linked to pronunciation (segmentals, word stress, intonation). The relative strength of these associations also varied as a function of the speakers' L1, especially for comprehensibility, with Chinese speakers influenced chiefly by pronunciation variables (segmental errors), Hindi speakers by lexicogrammar variables, and Farsi speakers showing no strong association with any linguistic variable. Results overall suggest that speakers' L1 plays an important role in listener judgments of L2 comprehensibility and that instructors aiming to promote L2 speakers' communicative success may need to expand their teaching targets beyond segmentals to include prosody‐, fluency‐, and lexicogrammar‐based targets.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".