Comparative Analysis of Canada (Ontario) and Turkey Social Studies Curriculum
Bibliographic record
Abstract
In this study, the Canadian Ontario Province and Turkey’s 2018 social studies course curricula were compared in terms of target, content, educational status, and evaluation dimensions. In addition, the study addressed the types of citizens that the two curricula intended to create. A case study method, one of the qualitative research methods, was used in the study. The data obtained in the study were analysed with the descriptive analysis approach within the framework of the determined sub-objectives. In Ontario, as in Turkey, social studies courses cover primary and secondary education levels. The Ontario social studies curriculum aims to enable students to become responsible, active citizens by exploring their identities in the context of the diverse (local, national, and global) communities to which they belong. In the Ontario social studies curriculum, it is evident that a national identity that reflects cultural diversity and equality is trying to be built. On the other hand, the Turkish social studies curriculum aims to raise students as good and responsible citizens who adopt national and spiritual values, emphasising a national sense of belonging. The study found that a personally responsible citizenship vision was adopted in Turkey and a participatory and justice-oriented citizenship vision was adopted in Ontario.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".