Assessing English Learners’ Knowledge of Semantic Prosody through a Corpus-Driven Design of Semantic Prosody Test
Bibliographic record
Abstract
This paper introduces a corpus-driven measure as a method to assess EFL learners' knowledge of semantic prosody. Semantic prosody here is defined as the tendency of some words to occur in a certain semantic environment. For example, the verb ‘cause’ is associated with unpleasant things—death, problem and the like. Subjects were 60 Iranian Persian-speaking English learners drawn from 180 candidates taking English classes in five language institutes. To estimate the quality of the test, a 70-item test of semantic prosody was constructed, validated, and used to measure the subjects’ knowledge of semantic prosody. The items were selected from COBUILD Dictionary and were mainly based on those cases of semantic prosody whose conditions (positive or negative) had been already determined by researchers. A proficiency test was applied to determine learners’ level of language proficiency as a variable which may affect the results. Data analysis showed that learners’ knowledge of semantic prosody is, and can be, appropriately measured by the corpus-driven test of semantic prosody. The implications of the findings for teachers, learners, and test developers are discussed.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".