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Record W2429589098 · doi:10.5539/jel.v5n3p193

Relation between Democratic Leadership and Organizational Cynicism

2016· article· en· W2429589098 on OpenAlexvenueno aff
Ali Rıza Terzi, Ramazan Derin

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicEducation Practices and Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsCynicismSeniorityPsychologySocial psychologyLeadership styleDemocracyScale (ratio)Political sciencePolitics

Abstract

fetched live from OpenAlex

This study intends to analyze the relation between school principals’ leadership styles and teachers’ perception of organizational cynicism. The study group consists of 268 participants teaching at high schools in the Balıkesir district of Turkey during 2014-2015 academic year. In the study, which used relational survey model, data was collected through Democratic Leadership Scale and Organizational Cynicism Scale and analyzed by mean scores, independent t-test, one-way analysis of variance (ANOVA), and simple linear regression. The results revealed that democratic leadership is a significant predictor of organizational cynicism, and it is negatively connected with organizational cynicism. In addition, it was found that there were significant differences between seniority and gender groups as regards democratic leadership, between seniority groups as regards all dimensions of organizational cynicism, and between gender as regards affective cynicism. All the results of the study showed that the democratic leadership style displayed by school principals influences the organizational cynicism perceived by teachers.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.292
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations36
Published2016
Admission routes1
Has abstractyes

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