Prospects for Bilingual Education Curriculum in Turkey: A Mainstream Issue
Bibliographic record
Abstract
The goal of bilingual education is fostering academic achievement, assisting immigrant acculturation to a new community, enabling native speakers to learn a second language, conserving linguistic and cultural heritage of minority groups, and advancing national language resources. This study investigated how certain parameters such as the views and attitudes towards bilingual education and curriculum development may affect the development of a bilingual education curriculum in Turkey. This study is significant because it could pave the way for developing a bilingual education program in Turkey. This study used an explanatory sequential mixed method, conducted in two phases: a quantitative phase followed by a qualitative phase. For quantitative data collection, 140 participants responded the survey instrument. For qualitative data collection, 4 participants were interviewed. The results indicated that a bilingual education curriculum is necessary for the education system in Turkey because the population of minority peoples is quite large. Results also reflected that a bilingual education program in Turkey should focus on speaking, listening, writing, reading, and on the development of vocabulary. Universities should open language teacher training departments for teachers who are going to teach in two languages. Examining and implementing elements of bilingual education models from other countries could prove helpful in establishing an efficient bilingual education program in Turkey.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".