An Examination of the Courses for the Education of Exceptionally Talented Children in Special Education Teaching Programs in European Union Member States and Turkey
Bibliographic record
Abstract
Legal regulations about education and educational policies are important for the quality of the practices. It has been seen that many countries invest in the education of the individuals who will form a qualified society in the future and focus on the teacher training and quality. The teachers have a great role in determining the exceptionally talented individuals in Turkey, their education, directing their performances in accordance with their interests and talents, enabling them to benefit from support education. Accordingly, in order to determine what kind of competency the teachers gain regarding this pre-service area, the courses in the special education teaching undergraduate programs regarding the exceptionally talented area in European Union (EU) and Turkey have been investigated in terms of number, term and credit content. Qualitative research method and document analysis technique are used in the descriptive study which aims to determine an existing situation. The data were obtained from education reports published by EU member countries about their own education systems, United Nations Children Rights and Education reports and Council of Higher Education’s Teacher Training Programs. For example, the preservice teachers are given theoretical information in the first classes, then receive education in which they can observe and show effective teaching skills with a mentor in the following years in the countries such as Belgium, the Czech Republic, Finland, Spain, Sweden, Luxemburg, and Malta.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".