Thai University Students’ Use of Yes/No Tokens in Spoken Interaction
Bibliographic record
Abstract
Adopting the interactional linguistic framework, the study aimed at exploring the range and frequency of interactional functions of yes/no tokens used by Thai university students of A2 proficiency in their English conversation, and contrasting their use with that of English native speakers (ENSs). The data was derived from 83, two-three party role-play conversations of approximately three–five minutes long obtained from conversation classes that were transcribed and analyzed. The findings revealed the students’ use of yes tokens in the following order of functional frequency: acceptance, confirmative response, positive alignment, acknowledgment, topic shift and self-confirmation. By contrast, no tokens were employed most often to disconfirm/disagree, followed by doing disappointment, restatement and negative alignment. Additionally, the students appeared to overuse yes tokens to fulfill certain functions for which ENSs usually deployed other expressions, and had difficulty giving grammatical short answers with the tokens. Furthermore, unlike ENSs, they often used these tokens alone, repeatedly or redundantly with other expressions of the same functions. It was suggested that students be made aware of grammatical expressions that can co-occur with yes/no tokens in giving short answers, and especially of a wider range of expressions commonly used in a specific context and various contexts in which an expression can be appropriately used.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".