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Record W3011087007 · doi:10.5539/hes.v10n2p95

Academics’ Opinions on Organizational Democracy and the Perception of Academic Freedom and the Appropriate Level in Turkey

2020· article· en· W3011087007 on OpenAlexvenueno aff
Süheyla Bozkurt, Ali Balcı

Bibliographic record

VenueHigher Education Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Governance and Development
Canadian institutionsnot available
FundersAnkara Universitesi
KeywordsDemocracyAcademic freedomLikert scaleTurkishAppropriationSociologyHigher educationPublic relationsPolitical sciencePsychologyPoliticsLaw

Abstract

fetched live from OpenAlex

The study aimed to determine how lecturers have adopted organizational democracy and academic freedom in Turkish state universities, as well as to find out their views on its appropriation for Turkey. A correlative descriptive survey model is used in this study. The study sample was included 418 academics working at state universities in Ankara. For this research, an ‘Organizational Democracy Scale’ and an ‘Academic Freedom Scale’ were developed and employed by the researcher, and the obtained data were analyzed using SPSS statistical software. The arithmetic mean and the standard deviation were calculated for the answers provided by the academics. Regression analysis was conducted to find out the effect of organizational democracy on academic freedom. In the qualitative phase of the study, face-to-face interviews were conducted with 20 academics. NVivo 10 software was used to analyze the content of the interviews. At the end of the study, it was found that university administrations should avoid discriminative attitudes; should account for their decisions and implementations; deans and faculty boards should be elected democratically; all stakeholders (apart from academics), including students, should take part in both elections and be involved in management processes; and finally, the election system should be more democratic.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.080
GPT teacher head0.367
Teacher spread0.287 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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