Discourse Markers in Composition Writings: The Case of Iranian Learners of English as a Foreign Language
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 aim of this study was to investigate discourse markers in descriptive compositions of 90 Iranian students who were selected from two universities. Without any instruction, they were given a topic to write a descriptive composition per week for 8 weeks. 598 compositions were collected, and they were analyzed qualitatively and quantitatively by three raters following Fraser's (1999) taxonomy of Discourse Markers. Findings showed that students employed discourse markers with different degrees of occurrence. Elaborative markers were the most frequently used, followed by inferential, contrastive, causative, and topic relating markers. There was a direct and positive relationship between the quality of the compositions and the number of well-functioned discourse markers. Results also revealed statistically significant differences between the use of discourse markers and composition quality in the groups. Graduate students used more discourse markers, and this led to more cohesive texts.
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 | 0.001 | 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 | 0.001 |
| 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 it