Understanding the Social Studies Curricula in Turkey as a Political Text Within the Context of Citizenship Education: Views of Academics and Teachers
Bibliographic record
Abstract
This study attempted to understand the curricula in conveying the state’s own understanding to individuals, according to the reconceptualization approach. As content, the social studies curricula (SSC), with the assumption that political influence would be seen most in these curricula, were examined. This study aims understanding the social studies curricula as a political text within the context of citizenship education in order to see how politics affect these curricula. To determine what political factors affected SSCs in which way, the opinions of academics and teachers were examined regarding curricula from 1998, 2005 and 2018, prepared during different government periods in Turkey. It was tried to determine how the changes in SSCs were defined in political/non-political dimensions, explanation and definition, the criticism, reasons and recommendation regarding these changes. This study was designed as a case study, one of the qualitative methods. Data analysis was done by content analysis method. It was determined that the changes in social studies curricula in 1998, 2005 and 2018 were affected by different political reasons and that there were some prominent ideological elements in all 3 curricula. As a result, it was determined that political effect on SSCs prepared in different government periods and can be seen radical changes were made in terms of curriculum structure and content from 1998 to 2005 and that the SSC of 2018 is similar to that of 2005 in terms of structure.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".