The Use of Lexical Bundles in English Language Academic Writing among University Learners: A Systematic Literature Review
Bibliographic record
Abstract
Lexical bundles have been widely studied in English academic writing, but not as extensive in university learners’ academic writing. This study conducted a systematic review of the literature on lexical bundles in academic writing among university learners from 2017 to 2022 to describe the directions of and limitations of recent studies on how lexical bundles influence the fluency and coherence of academic writing among university learners. The review relied on two major databases, Scopus and Web of Science, and adhered to the PRISMA 2020 guidelines. The study analyzed 28 articles them based on three content-based themes: the research context, the research contents, and the research objectives. Recent research on lexical bundles in academic writing among university students 1) lacks an in-depth analysis of specific functions, such as text-oriented bundles, which predominate and play an important coherence role in more advanced academic writing; 2) addressing the analysis of lexical bundles solely through a phraseological lens fails to account for their genre-specific characteristics. It is crucial to merge insights from both genre and phraseology for a thorough analysis; 3) comparing the academic writing of native English-speaking learners, that of university learners in East Asia and the Middle East are unevenly distributed and limited in scope. There is limited research on academic writing at higher academic levels, university learners in Southeast Asia, and cross-regional comparative studies.
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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".