Comparison of Turkey and the Countries with High PISA Success South Korea, Finland and Canada in Terms of Transition Education Levels
Bibliographic record
Abstract
It is aimed to compare the countries of South Korea, Finland and Canada, which are highly successful in PISA exam, and Turkey in terms of the transition system between educational levels. Document analysis method was used in the study. In the universe of the study, there are thirty-eight countries from the Organization for Economic Cooperation and Development. Turkey and the countries of South Korea, Canada and Finland which are profoundly effective in PISA, constitute the sample of the study. According to the results of the study, it has been determined that there are no exams in transition from pre-school education to primary education in Turkey, Canada, Finland and South Korea. There is a strict examination in the transition system from primary to secondary education level in South Korea, while there are no central exams in Canada and Finland, and transition to secondary education is made according to secondary school student success. In Turkey, a central examination is held for the transition from primary to secondary education level or students are accepted to secondary education by local placement, considering the addresses of the students according to their success. In Turkey and South Korea, a central exam is applied in the transition system from secondary education to higher education.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".