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Record W2920882827 · doi:10.5539/jel.v8n2p231

Examination of Attitude Levels of Academic Personnel Towards Political Participation and Determination of Its Relationships with Organizational Commitment

2019· article· en· W2920882827 on OpenAlexvenueno aff
Ersan Tolukan, Yakup Akyel

Bibliographic record

VenueJournal of Education and Learning · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsFeelingScale (ratio)PoliticsAlienationOrganizational commitmentPsychologyDescriptive statisticsInstitutionSocial psychologyPublic relationsSociologySocial sciencePolitical science

Abstract

fetched live from OpenAlex

The aim of this study was to determine the attitudes of academic personnel working in faculties of sports sciences in universities towards political participation; to examine whether there was a significant difference between the attitudes of academicians towards political participation according to demographic characteristics; to determine the relationships between academic personnel’s attitudes towards political participation and their organizational commitment. The study, which was designed in the correlational survey model of the general survey models, was carried out together with 204 academicians working in universities in Central Anatolia Region. The data of the study were collected by means of using the Political Participation Scale and Organizational Commitment Scale. Descriptive statistics, difference tests and Pearson correlation coefficient were used in the process of data analysis. At the result of the research, it was determined that the feelings of political activity and the feelings of alienation of academic personnel as well as their political perceptions arising from the institution were generally at a medium level. It was determined that there was a positive relationship between the feelings of political activity and the dimensions of organizational commitment of academicians.

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 score0.541
Threshold uncertainty score0.357

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 studies0.0000.000
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 head0.115
GPT teacher head0.344
Teacher spread0.229 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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