Bibliographic record
Abstract
This study examines the productive knowledge of synonyms in English by 40 Saudi EFL learners. It also tests whether the participants’ English proficiency level plays a role in their production of English synonyms. To this end, the researcher designed a translation test to measure Saudi EFL learners’ ability to produce the correct synonym in contextualised English sentences. In order to test whether the English proficiency level of the participants influenced their production of English synonyms, the participants were divided, on the basis of their scores on the Oxford Placement Test, into two groups: 20 Advanced Learners (ALs) and 20 Intermediate Learners (ILs). The answers of the two groups on the translation test, i.e., the ALs and ILs were compared to check whether their English proficiency level played a role on their answers. A Chi-square test was employed to determine whether the differences between the ALs and ILs on the test were statistically significant. The results show that the number of correct answers provided by ALs was higher than that provided by ILs, suggesting that their English proficiency level may have played a role in their answers. The study suggested that the main sources of error were L1 interference, lack of focus on the acquisition of vocabulary in schools in Saudi Arabia, lack of knowledge of some English lexical items, lack of awareness of the different nuances of meaning between the synonyms in English and lack of knowledge with English collocations. Finally, the study concludes with some recommendations for further 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".