The Use of Semi-automatic Annotation in Speech Acts Performed by Learners of English
Bibliographic record
Abstract
Since rare research studied speech acts (SAs) from a macro perspective, this study used a semi-automatic annotation tool named Dialogue Annotation and Research Tool (DART) to examine the most frequently performed SAs by Chinese and Thai learners of English as a foreign language (EFL). It also attempted to reveal the reasons for the similarities and differences in the SA performances of EFL learners from discrete linguacultural backgrounds. This study involved 30 Chinese and 30 Thai EFL learners, totaling 60 participants. A total of 30 dyadic English interlanguage conversations were collected for this study. The learner corpus research with the contrastive interlanguage analysis was adopted for the analysis. The DART annotation revealed that both Chinese and Thai EFL learners most frequently performed the same six SAs. These SAs were labeled by DART as state, hesitate, reqInfo, answer, expressOpinion, and stateReason. Of the six listed SAs, both Chinese and Thai EFL learners most frequently performed the SA labeled state; both cohorts also presented the SA stateReason least often. Task requirements, the frequent use of certain types of formulaic language, and English proficiency levels were ascertained as factors causing the identical presentation of the six identified SAs. The English proficiency levels of Chinese EFL learners were determined as the principal reason for their discrete performance of SAs. Conversely, the influence of the Thai linguacultural background denoted the primary factor for discrepancies in the SA performance of Thai EFL learners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".