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Record W2923153027 · doi:10.5539/jel.v8n2p264

Teachers’ Organizational Communication and Their Job Motivation

2019· article· en· W2923153027 on OpenAlexvenueno aff
Öznur Ataş Akdemir

Bibliographic record

VenueJournal of Education and Learning · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Leadership and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyData collectionScale (ratio)Regression analysisJob performanceOrganizational communicationSocial psychologyApplied psychologyJob satisfactionSocial scienceSociologyStatisticsMathematics

Abstract

fetched live from OpenAlex

The purpose of this study is to determine the effect of teachers’ organizational communication on their job motivation. The research model is correlational survey model. The participants of the study are 111 teachers working at public elementary schools in Turkey. Organizational Communication Scale and Job Motivation Scale are used as data collection instruments. For data analysis, Pearson correlation analysis and basic linear regression analysis are applied with SPSS. The findings of the study showed that teachers’ organizational communication and job motivation levels are at high level. Also, it is determined that there is a positive, high and significant relationship between teachers’ organizational communication and their job motivation. Besides, teachers’ organizational communication significantly predicts their job motivation. The findings of this study are discussed with related researches and some suggestions are presented.

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)0.008
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.030
GPT teacher head0.318
Teacher spread0.288 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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