Bibliographic record
Abstract
The aim of the study was to examine the lexical errors made by EFL students. The technique for eliciting information employed was an achievement test. A sample of 30 Saudi female students was asked to write essays in English that were assessed by the researcher. The students were all majoring in English in the third year at King Khalid University. James (1998) taxonomy was selected as the most comprehensive framework for the analysis of the lexical errors in the students' writing. A total of 137 lexical errors were identified and analysed. These errors were divided into formal 117 (85.40) and semantic 20 (14.60). Formal mis- selection 54 (39.42) was the most frequent major category of lexical formal errors while mis-formation 15 (10.95) was the least frequent one. Confusion of sense relations 14 (10.22) was the most frequent among lexical semantic errors. At the individual level of lexical formal errors, the most problematic words for students were the vowel based types 24 (17.52) and borrowing and blending were not problematic at all. At the individual level of lexical semantic errors, the most problematic words for students were near synonyms 8 (5.84) and the least problematic words were general terms for specific ones and overtly specific terms 1 (0.73).Pedagogical implications for teaching vocabulary to EFL learners and recommendations for areas for further research were suggested.
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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 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 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".