Analyzing Idioms and Their Frequency in Three Advanced ILI Textbooks: A Corpus-Based Study
Bibliographic record
Abstract
The present study aimed at identifying and quantifying the idioms used in three ILI Advanced level textbooks based on three different English corpora; MICASE, BNC and the Brown Corpus, and comparing the frequencies of the idioms across the three corpora. The first step of the study involved searching the books to find multi-word idiomatic expressions used in each. Idioms matching criteria for idiomaticity were selected and searched in the three online corpora to find their frequency of occurrence. Chi-square tests were then run to discover whether there were significant differences among the frequencies of occurrence of each idiom across each corpus. Having the number of idioms in each textbook, two other chi-square tests were then run, the first aiming at finding out if there were any significant differences among the three books in terms of idiom types and the second, to compare their tokens. The results showed that the books were different in terms of both number and type of idioms. It was also found that the idioms chosen for these Advanced level books did not meet necessary frequency criteria according to the literature, which could be attributed to representativeness issues of the corpora or their scope in terms of language level, genre and speaker’s age.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".