A Contrastive Study on the Utilization of Discourse Markers in the Discussion Section of English Research Articles
Bibliographic record
Abstract
Cohesion is an important aspect of English texts, and to achieve that, writers utilize certain devices known as Discourse Markers (DMs). This study thus sought to analyze the discussion section of 12 research articles (RAs) published on two different journals (i.e., Language Teaching Research and American Journal of Medical Genetics Part B: Neuropsychiatric Genetics) which cover contrastive disciplines (i.e., humanities and medicine) so as to find out the most used category of DMs. Additionally, the study aimed at identifying the differences between these two disciplines as to how DMs were utilized. The study thus adopted qualitative methods. That is, the analysis was based on Halliday and Hasan’s (1976) and Fraser’s (2009) classification frameworks of DMs. Results revealed that elaborative DMs were the most used category of DMs across all the RAs. Furthermore, the LTR corpus exhibited more varieties of DMs than the AJMG corpus in which DMs were less used. Lastly, some suggestions are given for future research that have interest in discourse analysis in general, and in contrastive analysis in particular.
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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 | 0.023 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".