Discourse markers in academic and non-academic writings of Thai EFL learners
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The ability to use discourse markers (DMs) to create cohesion and coherence of a text is essential for EFL learners at the university level to express ideas and thoughts in various types of writing assignments, such as academic papers and reflections. Hence, this study attempted to shed more light on the use of DMs in academic and non-academic writings of Thai EFL learners. The main objective was to investigate the types, overall frequency, and differences, and similarities of discourse markers in both styles of writing. Sixty essays, consisting of 20 academic essays and 40 non-academic ones, were selected as the primary data. Academic essays were selected from the Critical Reading and Writing course of Xavier Learning Community (XLC), Thailand, while the non-academic ones were selected from the XLC English Newsletter. The data were analyzed based on Fraser’s taxonomy (2009). The results showed that 2.521 DMs distributed in five types, namely contrastive discourse, elaborative discourse, inferential discourse, temporal discourse, and spoken discourse markers, were identified in the 20 academic and 40 non-academic essays. The most frequently used DM was elaborative discourse markers (EDM), F=1,703. This study concluded that raising awareness of DMs would assist Thai EFL learners in producing an effective and coherent piece of writing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it