Distribution of Hesitation Discourse Markers Used by Iranian EFL Learners during an Oral L2 Test
Bibliographic record
Abstract
Previous studies on hesitation strategies used by beginner or advanced L2 learners revealed that beginners mostly leave their hesitation pauses unfilled which causes their speech to sound disfluent, and advanced learners tend to use various fillers in order to sound like native speakers.The present paper reports on a study which investigated the distribution of hesitation discourse markers including silent pauses, silent pauses and fillers, fillers, and non-lexical words used by Iranian university students in an oral (L2) test. The study examines the location of the discourse markers of hesitation across utterances produced by the participants. The respondents were a group of students registered in the Tertiary English Language Program at a university in Kuala Lumpur, Malaysia. The aim was to identify the frequency of all hesitation strategies used in four locations of Initial, Middle, and Final position of the utterances to find out the most frequent location of hesitation during an oral (L2) test.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".