Lexical Bundles within English for Academic Purposes Written Teaching Materials: A Canadian Context
Bibliographic record
Abstract
Research surrounding lexical bundles in English for Academic Purposes (EAP) is fundamental to the academic success of students pursuing postsecondary education.Recent studies suggest that awareness of the presence and function of lexical bundles in academic English is beneficial to students and teachers, and leads to a deeper understanding of the construction of discourse (Biber, 2004).Using corpus linguistics, this study presents the degree to which students of an EAP program at a mid-sized Canadian university encounter four-word lexical bundles in their written teaching materials.The study uses functional taxonomy to classify the lexical bundles of the Moynié corpus I created.The results indicate the need for further research to assess the frequency of lexical bundles amongst all the registers that students come across in their academic studies.This is to ensure the students achieve fluency in English, and can competently understand, recognize and utilize lexical bundles.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".