Systematic Literature Review of Crosslinguistic Analysis of Stance Markers in EFL Learners’ Academic Writing in English
Bibliographic record
Abstract
Evaluating the use of stance markers is an important approach to investigate the interactional and persuasive nature of academic writing in English. However, the problems EFL learners’ face using of stance markers and its research methodologies in crosslinguistic analysis are uncertain. This systematic literature review examined problems EFL learners’ face in using of stance markers and the research methodologies used to identify these problems in crosslinguistic research. The current study employed the PRISMA 2020 paradigm to conduct a systematic literature review (SLR) of crosslinguistic analysis of stance markers in EFL learners’ academic writing in English. Keywords queries on “stance*”, “academic writing*”, “metadiscourse*” and “metadiscursive*” were used to retrieve articles from Scopus and Web of Science databases. Following screening, 34 articles were included in the final analysis. EFL learners had problems in using hedges, boosters, self-mentions and attitude markers, and their main challenges were in the overuse of boosters and underuse of hedges. Chinese EFL learners had the most problems in using stance markers. All the 34 articles adopted an empirical approach and most were a corpus-based study. Researchers were fond of argumentative essays, dissertations, and research articles from coursework. In addition, scholars preferred one-way comparisons especially between native language and interlanguage (NL vs IL). Enhancing EFL learners' awareness of stance markers would require more instructions regarding stance markers in academic writing classrooms and future research should use three-way or four-way comparisons in crosslinguistic analysis.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".