Bibliographic record
Abstract
As part of formulaic language, lexical bundles (LBs) have been a rich topic of study for the last few decades.Following a frequency-driven approach, the present corpus-based study aims to identify the most frequently used LBs in first year Economics textbooks used in a number of Canadian universities.It also aims to classify the LBs according to the functional taxonomy of lexical bundles suggested in Biber et al. (2004a).The First Year Economics Textbook Corpus (FYETC) is a specialized corpus built especially for the purpose of this study.The text analysis software, WordSmith Tools 6.0, was used to extract the LBs from FYETC.To verify the results, three expert judges reviewed the FYETC LBs list.The results show that referential bundles are dominant in FYETC, and that quantity reference is by far the biggest subcategory.The final product of the study is a list of 165 LBs classified functionally along with some suggestions to improve the performance of the functional taxonomy.The findings of this paper can be of interest to EAP/ESP teachers, curriculum designers, and/or business and economics students.
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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.025 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".