An Investigation into English Vocabulary Attrition among College Students of Non-English Majors in Inner Mongolia University for the Nationalities
Bibliographic record
Abstract
Since language attrition was formally recognized at the conference on the “Attrition of Language Skills” at the University of Pennsylvania in 1980, the past three decades saw the numerous studies and researches on it. Language attrition refers to a constant overall regression of language ability with decreased or ceased language use. Language attrition as the inverse process of language acquisition provides a brand-new perspective for language acquisition study. Thus, language acquisition study is incomplete without study on language attrition. Most of related researches and studies of language attrition have been finished in European, American and Japanese contexts. A good academic harvest is reaped by foreign scholars, such as Seliger, Sharwood, Bahrick, Hasan, Tomiyama, Gardner, Kopke, Lambet, Weltens and so forth. In China, despite of many achievements have been made in field of language attrition, little attrition has been paid to the language attrition study. Language attrition still stays at the theory-introducing stage. Empirical studies are sporadic. This paper will try to analyze English attrition degree from the perspective of vocabulary among college students of non-English majors in universities for nationalities, find whether attrition difference exists in English vocabulary, that is, which kind of words are more liable to be attrited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".