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Record W2162336370 · doi:10.5430/ijhe.v4n4p188

Policies and Practices of the Doctoral Programs in English Language Teaching in Turkey

2015· article· en· W2162336370 on OpenAlexvenueno aff
Kemal Sinan Özmen, Betül Kınık

Bibliographic record

VenueInternational Journal of Higher Education · 2015
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsnot available
Fundersnot available
KeywordsStandardizationHigher educationDoctoral studiesEnglish languageInterpretation (philosophy)Political scienceField (mathematics)PedagogySubject (documents)Graduate studentsMedical educationSociologyMathematics educationLibrary sciencePsychologyComputer scienceMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.235
GPT teacher head0.572
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2015
Admission routes1
Has abstractyes

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