Comprehensible to Whom? Examining Rater, Speaker, and Interlocutor Perspectives on Comprehensibility in an Interactive Context
Bibliographic record
Abstract
Abstract Comprehensibility has emerged as a useful and intuitive means of globally evaluating second language (L2) speakers in many research and instructional contexts. In most cases, L2 speakers’ comprehensibility is assessed by external listeners who do not engage in extensive communication with the speakers, even though the degree to which a speaker is comprehensible is presumably of greatest concern to their interlocutor. If comprehensibility is defined as the ease with which speakers come to understand one another, then interaction‐based assessments, which would include self and peer ratings, might provide different insight into interactive comprehensibility compared to assessments by external listeners. To examine this issue, in this study, 20 pairs of L2 English interactants rated themselves and their partner on 7 occasions distributed throughout a 17‐minute interaction encompassing 3 communicative tasks, and recordings of the interaction were subsequently presented to external raters for evaluation. Mixed‐effects models were used to compare the shape of the comprehensibility curves over time and the self, partner, and rater scores at each rating episode. Results demonstrated that self and partner assessments were always aligned, but raters consistently assigned significantly lower comprehensibility scores to the interactants. These findings have implications for how comprehensibility, and indeed other listener‐based constructs, are assessed.
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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 | 0.018 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".