Comparison of Turkey and Countries with High PISA Achievement in terms of Education Expenditures and Academic Achievements
Bibliographic record
Abstract
The aim of this research is to compare the education expenditures of the Economic Cooperation and Development Organization (OECD) countries such as Canada, Finland and South Korea, which are successful in the 2018 cycle of the Program for International Student Assessment (PISA) with Turkey, and to make suggestions for Turkey. The research was designed in the scanning model. The research population is 36 OECD countries. Canada, Finland and South Korea which were succeed in the 2018 PISA and Turkey were selected as samples. Data were obtained from OECD reports on the 2018 PISA cycle and other international and national documents. In the research, document analysis method was used. According to the results of the research, it has been seen that the education expenditures of Canada, Finland and South Korea are high and the countries are successful in PISA. Therefore, it can be said that there is a positive relationship between PISA achievement and education expenditures. Public education expenditures only in higher education in Turkey are higher than Canada, Finland and South Korea and the OECD average. Education expenditures per student in Turkey are below the OECD average and almost a third of that of Canada, Finland and South Korea. There are significant differences when comparing Turkey with other countries (Canada, Finland and South Korea) in terms of Gross Domestic Product (GDP), expenditure per student and teachers' salaries.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".