Lexical Bundles in Argumentative and Narrative Writings by Chinese EFL Learners
Bibliographic record
Abstract
Previous studies have shown that lexical bundles are important building blocks of discourse and a significant component of fluent linguistic production. However, little research was found to investigate lexical bundles in narrative writings, a basic text type on which the other text types (discourses) build upon. The present study tries to fill the gap and investigates lexical bundles in argumentative and narrative writings by Chinese EFL learners. The lexical bundles were retrieved by kfNgram and then manually refined and classified into structural and functional categories respectively based on Biber et al.’s (1999) and Biber et al.’s (2003) frameworks. The findings show that (1) students used much more four-word bundles in argumentative writings than those in narrative writings; (2) no big difference was found in the structural patterns of the four-word lexical bundles used by the students across the two text types; (3) students relied much more on stance bundles than the other functional types of bundles in their argumentative writings, while they turned to referential expressions other than stance bundles or discourse organizers in their narrative writings. The functional purposes of various discourses explain the students’ selection of different functional patterns across the text type.
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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 | 0.002 | 0.013 |
| 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 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".