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Record W2898304186 · doi:10.5539/hes.v8n4p129

Academicians in Turkey: An Evaluation of Current Status of Academic Staff in Higher Education

2018· article· en· W2898304186 on OpenAlexvenueno aff
Bertan Akyol, Filiz Tanrısevdi

Bibliographic record

VenueHigher Education Studies · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
Fundersnot available
KeywordsTurkishHigher educationVocational educationContext (archaeology)Turkish republicQuality (philosophy)Political scienceMedical educationEconomic growthPedagogySociologyMedicineGeographyLaw

Abstract

fetched live from OpenAlex

The history of Turkish higher education dates back to Turkish nations of 1000 years ago. The beginning of higher education institutions are accepted as madrasahs that continue its existence during the Ottoman period. After the foundation of the Turkish republic, rapid changes and developments have been observed in the higher education like in all fields. Since this period of time, Turkish higher education institutions have been grouped in two categories, which are universities producing information-knowledge and vocational schools training people oriented with employment. Considering the both types of these institutions, the aim of Turkish higher education system is to sustain manpower considering the needs of the nation and the public; provide education and training facilities based on the secondary education; maintain the quality and quantity of scientific researches under the control of universities. In this context, the purpose of this study is to present the certain dimensions of Turkish higher education system, which are academicianship, current facts related to academicianship, the stages in academic career, achievements in academy and the status of women academicians in Turkey. Related documents have been analyzed and the current status of Turkish higher education system has been discussed by concluding the results.

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)0.002
metaresearch head score (Gemma)0.000
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 score0.276
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.206
GPT teacher head0.505
Teacher spread0.299 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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