Policies and Practices of the Doctoral Programs in English Language Teaching in Turkey
Bibliographic record
Abstract
The present review focuses on the doctoral programs and dissertations in the field of English language teaching between the period 2010 and 2015 in Turkey to reveal how the latest reforms on higher education shaped the programs, supervisors, students and dissertations. This research focus requires immediate attention as there is not yet an established body of literature addressing how the National Qualifications Framework of Higher Education, as the national interpretation of the European higher education policies, works in practice across doctoral programs. To this end, 13 graduate programs on English language teaching were analyzed in 2014-2015 academic year in terms of program structure, courses, supervisors and students. In addition, 137 doctoral dissertations written in those programs between 2010 and 2014 were investigated with regards to their subject areas and research focus and how those two factors were distributed across the programs. Findings indicate that although the national qualifications framework seems to contribute to the programs significantly in terms of standardization, nation-wide policies are necessary to expand the impact of doctoral dispositions beyond academia, and that further research studies are needed to yield data about the scholarly impact of those doctoral programs.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".