A Comparison of Social Studies Curriculum: A Case of South Korea and Turkey
Bibliographic record
Abstract
Countries use education as a tool to shape the desired human profile in line with universal principles. For this purpose, the courses in the curriculum have specific objectives. Social studies is an important course in the curriculum of countries for the purpose of raising “qualified people”. This course is included in the curriculum of many countries around the world (Canada, Japan, South Korea, Finland, Turkey, etc.) due to its mission of “realizing social existence”. The aim of this study is to compare the South Korean Social Studies Curriculum updated in 2015 and the Turkish Social Studies Curriculum updated in 2017. The data of the research, which was carried out by adopting the qualitative research approach, were obtained through document analysis. The data obtained from the research were analyzed according to the document analysis stages. When the results of the research are evaluated in general, it is seen that South Korea Social Studies Curriculum and Turkey Social Studies Curriculum are similar in terms of purpose, content (geography and economics topics) and achievement code system. In South Korea, the Social Studies course takes place at the primary, secondary and high school levels, and in Turkey it takes place at the primary and secondary school levels. It has been determined that the social studies course hours in the grade levels of both countries also differ.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".