A comparative analysis of educational administration doctoral programs in the international context: Examples from Türkiye and Canada
Bibliographic record
Abstract
This study aims to compare the structural features and program contents of educational administration doctoral programs in Türkiye and Canada, both of which are ranked among the top 1000 universities in the 2023 Academic Ranking of World Universities (ARWU) list. This study is an example of comparative educational research as it aims to identify, compare, and analyze similar and different characteristics of the selected doctoral programs in Canada and Türkiye. The study sample consists of six universities, three from Türkiye (n=3) and three from Canada (n=3), selected by the stratified purposeful sampling method and positioned at different points in the ARWU ranking system. Therefore, the University of Toronto, the University of Alberta, and the University of Saskatchewan from Canada; and Hacettepe University, Ankara University, and Ege University from Türkiye, formed the study sample. The data was collected through the document analysis method and analyzed using the descriptive analysis method. Firstly, online information and documents were obtained from the official websites and graduate admission units of the doctoral programs at the selected universities. Secondly, the selected doctoral programs in Türkiye and Canada were analyzed by comparing their admission requirements, degree requirements, and offered academic courses. The results revealed similarities and important differences between the selected doctoral programs in the two countries. Considering educational administration doctoral programs at the top-ranked Canadian universities, this study suggests potential innovations that could be implemented to establish successful and outstanding educational administration doctoral programs in Turkish universities.
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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 | 0.007 | 0.018 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| 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".