Bilingual Curriculum Construction and Implementation of Preschool Education in Tibetan Areas
Bibliographic record
Abstract
Preschool bilingual curriculum development is a hot issue of preschool education in ethnic areas of China. This study used in-depth interviews and observations, were studied in the bilingual education in 23 kindergarten Tibetan area. The purpose of the study is to understand the current status of preschool bilingual education curriculum development in Tibet area, so as to analyze the construction and implementation of preschool bilingual education curriculum .The study found, preschool bilingual curriculum construction in Tibetan areas has made remarkable achievements, such as the Tibetan cultural characteristics of thematic curriculums, the form of the game teaching methods, immersion language communication modes, and visual forms of parent participation in kindergarten education. The survey also found the problems of preschool bilingual curriculum construction and implementation, such as the content of the curriculum does not reflect the characteristics of Tibetan area, teaching methods ars unsuitable for children’s learning requirements. The researchers believe that, preschool bilingual education curriculum construction must correctly understand the special nature of bilingual education. The relationship between Tibetan culture inheritance and the development of education is coordinated, so that the quality of preschool education will be improved.
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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 | 0.002 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".