The Example of Teaching False Equivalent Words in Teaching Turkish to Kyrgyz
Bibliographic record
Abstract
Frequent use of common words in teaching Turkish to Kyrgyz helps to increase students’ attention to the lesson. However, some words may cause translation problems because their spelling and pronunciation are the same but their meanings are different. In this framework, it is important to take into consideration the false equivalents between dialects when teaching Turkish to Kyrgyz students. In this study, activities for teaching false equivalents are proposed. In line with this purpose, the research was designed with a qualitative approach and it was aimed to make students recognize false equivalents at A1 basic level by interacting in the classroom. When selecting false equivalents, the frequency of use in the target language was carefully considered and it was planned to teach the related words by role-playing them. As a result of the study, it is expected that students will recognize false equivalent words and show interest in dialogue activities. In addition, it is also thought that it will provide interaction among students. It is thought that the study will contribute to the teaching of false equivalent words.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".