Lexical Attrition of General and Special English Words after Years of Non-Exposure: The Case of Iranian Teachers
Bibliographic record
Abstract
This study sought to investigate the rate of attrition in general and special vocabulary in and out of context. Participants of the study were 210 male Persian literature teachers with different years of non-exposure to English (2, 4, 5, 6, 7, 8 &10) after graduating from university. They were selected through purposive sampling from among 1000 Persian literature teachers from three provinces, namely Esfahan, Fars and Yasuj. Their age ranged between 25 and 35. The instrument included one translation task. The task consisted of 20 items of general words and 20 items of special words which were tested in and out of context.Results indicated that the rate of attrition increased gradually as the years of non-exposure to English increased. Also, it was found that the rate of attrition in special and contextualized lexicon is respectively less than general and de-contextualized lexicon.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
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".