Lexical Bundles in English Language Textbooks: A Systematic Review
Bibliographic record
Abstract
Lexical bundles (LBs) are recurrent continuous sequences of two or more words that frequently appear in discourse. Acquiring these bundles helps compensate for short-term memory limitations, supports efficient language processing, and promotes both comprehension and production in English. Lexical bundles can be learned through English language (EL) textbook input. Numerous researchers advocate for the inclusion and emphasis of native-like lexical bundles in EL textbooks; however, relatively few studies have evaluated lexical bundles in these textbooks. This study seeks to review the literature on lexical bundles in EL textbooks over the past two decades (2004-2024) to identify research trends and gaps. The review relied on three databases: Web of Science Core Collection, Scopus, and ERIC. After a rigorous screening process, 18 relevant studies were identified and coded based on study identification, language context, research aims, textbook types, methodologies, target lexical bundles, and main findings (cf. Appendix A). The findings recommend future research in this domain continue to employ the corpus-assisted method to investigate lexical bundles in EL textbooks and conduct a comprehensive evaluation using multiple analytical dimensions to explore three- and four-word bundles in foundational-level English for General Purposes (EGP) textbooks and English for Specific Purposes (ESP) textbooks.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".