Bibliographic record
Abstract
Abstract The relationship between second language (L2) comprehensibility and pragmatics is explored in two experiments involving instruction of speech acts to learners enrolled in a Language Instruction for Newcomers to Canada program. The study was designed to determine whether improved pragmatic competence results in enhanced comprehensibility (how easy L2 speech is to understand). Two intact classes participated; one received 25 hours of pragmatics instruction, while the control group received the standard curriculum (no focus on pragmatics). Both classes were recorded in role-plays based on several scenarios at pre- and post-test. Transcriptions of the role-plays were coded according to a rubric; although the control group showed superior performance at the outset, the experimental group’s scores exceeded those of the Control group at post-test with a medium effect size. A subset of pre- and posttest role-plays (two refusals and two requests) were randomly assigned to 56 native English listeners who rated the speech samples for social appropriateness, comprehensibility, and fluency. The experimental group’s posttest productions on all scenarios were perceived as significantly more socially appropriate, with three scenarios showing significant improvement in comprehensibility. Although one scenario improved in fluency, another showed a decline. The results suggest that pragmatics instruction enhances L2 speech comprehensibility.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".