Framing second language comprehensibility: Do interlocutors’ ratings predict their perceived communicative experience?
Bibliographic record
Abstract
Abstract Comprehensibility has risen to the forefront of second language (L2) speech research. To date, research has focused on identifying the linguistic, behavioral, and affective correlates of comprehensibility, how it develops over time, and how it evolves over the course of an interaction. In all these approaches, comprehensibility is the dependent measure, but comprehensibility can also be construed as a predictor of other communicative outcomes. In this study, we examined the extent to which comprehensibility predicted interlocutors’ overall impression of their interaction. We analyzed data from 90 paired interactions encompassing three communicative tasks. Interactive partners were L2 English speakers who did not share the same native language. After each task, they provided self- and partner-ratings of comprehensibility, collaboration, and anxiety, and at the end of the interaction, they provided exit ratings of their overall experience in the interaction, communication success, and comfort interacting with their partner. We fit mixed-effects models to the self- and partner-ratings to investigate if those ratings changed over time, and we used the results to derive model-estimated predictors to be incorporated into regression models of the exit ratings. Only the self-ratings, including self-comprehensibility, were significantly associated with the exit ratings, suggesting a speaker-centric view of L2 interaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".