Bibliographic record
Abstract
One sub-type of collocations which is under-researched is binomials. The purposes of this study were to investigate Thai EFL students' knowledge of English binomials, determine the syntactic structure of the internal elements of binomials reported as most or least known by them, and test if the students' knowledge of binomials was significantly different when taking account of their years of study. The sample was 130 first - sixth year students enrolled in four different faculties at a university in northeastern Thailand, and classified as intermediate EFL learners for the purpose of this study through the online Oxford Placement Test. An acceptability judgment test of English binomials was used to collect the data. The results revealed that, on the whole, the participants had a low level of knowledge of English binomials; there was no significant difference in their knowledge regarding the syntactic structure of binomials (Noun+Noun and Verb+Verb); and the participants were not significantly different in their knowledge in terms of their years of study. The results pertaining to the participants’ low level of knowledge of binomials were discussed in relation to lack of exposure to English and effective pedagogy of English idioms.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".