Bibliographic record
Abstract
The mass migration of Syrians with a high rate of school-age children into Turkey brought together the need for teaching these students Turkish for integrating them into the society and Turkish education system. The Ministry of National Education gave this responsibility especially to Turkish teachers. Therefore, these teachers should have the required pedagogical formation and skills to teach Turkish to these students, as well as intercultural sensitivity and cultural knowledge about them. In this study, 19 newly graduated Turkish teacher candidates from a state university in Ankara from the Turkish Education Department were interviewed with the aim to gather their views about Syrian students, their readiness if they are to teach them and their evaluation of their pre-service education in terms of preparing them to this kind of teaching. The qualitative data were collected via the semi-structured interview form prepared by the researcher and was analyzed with descriptive analysis method. The results show that Turkish teacher candidates do not think they are definitely ready to teach Syrian students. However, they have positive attitudes and believe in themselves that they will do their best to teach them. Teacher training programs should be updated according to the multicultural structure of the schools with Syrian students and these programs should provide the pre-service teachers with the required current information about the student profile in schools.
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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 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".