To What Extent Are Multiword Sequences Associated With Oral Fluency?
Bibliographic record
Abstract
Abstract This study examined the relationship between oral fluency and use of multiword sequences (MWSs) across four proficiency levels (Low B1 to C1 of the Common European Framework of Reference). Data came from 56 learners taking the speaking test of the Test of English for Educational Purposes, and our analysis obtained different measures of fluency (speed, breakdown, repair) and MWSs (frequency, proportion, association). Results showed that (a) high‐frequency n‐grams correlated positively with articulation rate; (b) n‐gram proportion correlated negatively with frequency of mid‐clause pauses; and (c) n‐gram association strength correlated positively with frequency of end‐clause pauses and negatively with repair frequency. Qualitative analysis suggested that the test‐takers borrowed some task‐specific n‐grams from the task instructions and used them frequently in their performance. Whereas lower proficiency speakers used these n‐grams verbatim, C1 level speakers used them competently in a variety of forms. We discuss significant implications of the findings for phraseology and language testing research.
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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 | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".