A Corpus-Based Study on Chinese English Majors’ Use of Discourse Markers
Bibliographic record
Abstract
Discourse markers (DMs for short hereafter) are used widely by native speakers and L2 learners. Previous studies are mostly about the acquisition and application of DMs, but studies on the potential factors that might affect the use of DMs are rare. Under the Relevance Theory, the present corpus-based study aims to reveal the influence of gender and oral proficiency on the use of DMs by Chinese English majors. It found that: (1) generally speaking, the variety of DMs employed by Chinese learners of English is rather limited; (2) male Chinese learners of English use more DMs than female learners; (3) high proficiency Chinese learners of English employ more DMs than the low proficiency group; moreover, the variety of DMs used by the former group is much larger than that of the latter. These findings reveal that the learner factors of gender and oral proficiency do influence the use of DMs in L2 learners’ conversations and they should be taken into consideration when we are learning or researching.
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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 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".