Receptive Vocabulary Size of Male and Female Saudi English Major Graduates
Bibliographic record
Abstract
This study measured Saudi university students’ receptive vocabulary knowledge towards the end of their final semester. The subjects were 71 Saudi male and female students. The Vocabulary Levels Test, adopted from Nation’s (2008), was administered in this study. The test assesses learners’ receptive knowledge of word meaning at the following distinct vocabulary levels: the 2nd 1,000-word level, the 3rd 1,000-word level, the 5th 1,000-word level, the 10th 1,000-word level, and the Academic Word List (AWL). The results showed different participants’ performance at different word levels with decreasing mean scores as the frequency of word levels decreased. The results also showed, with no exception, that males outperformed females with statistically significant differences in all the five sections of the test. The participants’ average vocabulary size is approximately 876 and 799 words in the 2nd 1,000-word level, 436 and 355 words in the AWL, 725 and 590 words in the 3rd 1,000-word level, 580 and 477 words in the 5th 1,000-word level for males and females respectively. However, the average vocabulary size decreased dramatically in the 10th 1,000-word level to 254 words for males and 124 for females. Based on these findings, it is concluded that Saudi English Language and Translation university graduates, even with large vocabulary size in the high frequency bands, are generally still below the level of the desired vocabulary competency as EFL learners, and are in fact, in need for more support and concentration in their undergraduate study with regard to their vocabulary learning.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".