Comparison of Bilingual Education Programs and Models in Spain, Canada, USA, Turkey and Iran: A Comparative Study
Bibliographic record
Abstract
Bilingualism is one of the most important issues in the field of education, and today there are wide-ranging discussions about it. The purpose of this study is to compare the programs and models of bilingual education in Spain, Canada, USA, Turkey and Iran. The analytical method of documentary study is a comparative approach with the help of George Brady's model. For collecting data, a library study method including official educational documents, results of research and comparative studies was carried out, reports, seminars and information banks were used in this field. The results of the study showed that in Spain, the United States and Canada, they have provided educational background and professional success for bilingual children with the importance of minority students' rights. In contrast, Iran and Turkey have provided a synergistic education approach, a drop in education and a decline in bilingual children's self-esteem. In the countries of Spain, Canada and the United States, there are two-way educational models, immersion and transitional, while in Iran and Turkey, a structured model of educational learning is used. According to the research findings, it can be argued that the educational system of Iran can best use the experience of the above countries and the educational model that is more effective in successful countries in the field of bilingual education (French immersion programs in the system Canadian Bilingual Education, D-Model in Spain and Bilingual Education), and tailor-made the model to suit the needs of society, students and teachers in their country.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".