Vocabulary Learning of Socially Relevant/Irrelevant Texts by English Students of Islamic Azad University
Bibliographic record
Abstract
Learning English vocabulary has been considered somehow problematic at all levels. The present research endeavors to investigate the degrees of difficulty in the process of learning of new vocabulary in familiar and unfamiliar contexts for Iranian learners of English as a foreign language. In the first phase, 113 subjects were grouped into three English proficiency levels on the basis of their scores on the TEOFL Test; 40 at pre-intermediate, 38 at intermediate, and 35 at upper-intermediate group. Then they were given two texts to read; one about Halloween, relatively unfamiliar to Iranian students, and another text about Chaharshanbe Suri, a familiar issue for Iranian students. The subjects were to mark all unknown words and, if possible, guess the meanings of words. All successful guessing resulted from contextual clues were crossed out lest it should nullify the whole investigation. Statistical analysis of the participants' performance indicates the following: a) guessing new vocabulary in familiar and unfamiliar texts pose different levels of difficulty; unfamiliar texts being more difficult. b) This difficulty pattern is not affected by the proficiency level of the students.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".