Lexical Interference and Ways of Its Elimination: Based on Experience with Junior Course Students of the Azerbaijan University of Languages
Bibliographic record
Abstract
The problem of language interference being a process which retards the mastering of a second language, having appeared as a result of transference of speech skills from one contact language into another (from the native language into the foreign language, from the first foreign language into the second one), has concerned researchers for decades. This phenomenon has a direct influence on the success of an individual’s mastery of a foreign language and its use—involving both receptive and productive types of speech activities. Interference resulting from the negative impact of one language on another covers all linguistic levels of the language being studied, including lexical, which leads to deviations from the language norm and numerous lexical errors of students. Linguists and methodologists are trying to find ways to reduce the interference of the language being studied at the lexical level in order to optimize the process of mastering a foreign language and minimize lexical errors of students. The purpose of the current study is to investigate ways to overcome intra-language and inter-language lexical interference in junior courses of the Azerbaijan University of Languages and to verify the validity of these methods in the course of a practical experiment.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".