Dominant and Gender-Specific Tendencies in the Use of Discourse Markers: Insights from EFL Learners
Bibliographic record
Abstract
This study followed two objectives: it primarily investigated the types of discourse markers (DMs) used in thespoken language of Iranian advanced EFL learners, and then explored the possible impact of gender on theparticipants’ use of DMs. To this end, 40 male and female EFL learners selected from an English language instituteparticipated in this study. The data were gathered through class observations. The researchers used Fraser’staxonomy of DMs and Fung’s category of interpersonal DMs as the theoretical framework of the study. To analyzethe data descriptive and inferential statistics were used. Results of the frequency test revealed that “and” was themost commonly used elaborative DM, whereas “but” was the most frequent contrastive DM. “Because” and “by theway” were respectively the only reason and topic-related DMs used by the participants, while “sure” was the mostfrequent interpersonal DM. In addition, results of the chi-square test revealed that learners significantly employedinterpersonal DMs more than the other sub-classes of DMs. Concerning the role of gender in the use of DMs, resultsdemonstrated that females significantly used more DMs compared with the males.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".