Polysemous Phrasal Verbs: How Much Do Thai EFL High School Learners Know?
Bibliographic record
Abstract
This study examined Thai senior high school EFL learners’ receptive and productive knowledge of English polysemous phrasal verbs. The research employed the framework developed by Garnier and Schmitt (2015), which is the most widely acknowledged and functional concept of English polysemous phrasal verb knowledge. A battery of tests measuring the participants’ receptive and productive knowledge of English polysemous phrasal verbs and vocabulary size were administered. The results indicated that Thai EFL high school learners had an intermediate understanding of polysemous English phrasal verbs. On the receptive knowledge test, participants scored higher than on the controlled and uncontrolled productive knowledge assessments. In addition, the results demonstrated a positive correlation between vocabulary size and receptive / productive knowledge of English polysemous phrasal verbs. The correlation analysis also revealed that a number of English polysemous phrasal verb knowledge dimensions were interrelated. This study provides empirical evidence that Thai EFL learners’ knowledge of English polysemous phrasal verbs develops along a continuum from receptive to productive use. This research also suggests that polysemous phrasal verbs are multidimensional and progressive. Longitudinal experiments with varying L1 and education levels would be beneficial for future research.
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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 | 0.001 | 0.004 |
| 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 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".