Bibliographic record
Abstract
The ongoing changes in history education in support of diversity have an effect on Turkey even if on a limited scale. Although the current history curriculum in Turkey promotes the identity transmission instead of respecting different identities, it also has some goals such as “teaching the students about basic values including peace, tolerance, mutual understanding, democracy, and human rights, and making them sensitive about maintaining and improving these values”, which is compatible with the contemporary understanding of history education. However, it must be noted that the attitudes and perceptions of teachers are as important as their presence in curriculum in terms of reaching the aims of history education. The aim of this study was to reveal preservice history teachers’ attitudes towards identity differences. Identity Attitudes Scale (IAS), which was developed by Yazici (2016) to measure the attitudes towards identity differences, was conducted on 314 preservice history teachers. Preservice teachers’ attitudes towards identity differences in terms of gender, and their ethnic, religious and political identities were examined using t-test and one-way variance analysis. As a result, it was found that the variables had effect on preservice teachers’ attitudes at varying rates.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".