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Record W2214182783 · doi:10.5539/ies.v9n1p182

A Study of the Effect of Education and Academic Environment on Emotional Intelligence on Accounting Students in Iran

2015· article· en· W2214182783 on OpenAlexvenueno aff
Mahdi Salehi, Mohammadreza Abbas Zadeh, Alireza Ghaderi, Alaleh Zhian Tabasi

Bibliographic record

VenueInternational Education Studies · 2015
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsEmotional intelligencePsychologyBachelorTest (biology)Class (philosophy)Mathematics educationSignificant differenceAccountingSocial psychologyStatisticsPolitical scienceMathematics

Abstract

fetched live from OpenAlex

The current study aims to investigate the relation between education and academic environment on emotional intelligence of accounting students in state and non-state universities in Iran. In order to collecting data Bar-on emotional intelligence test and SCL 90 questionnaire administrated among 476 students in different subjects including accounting in both Bachelor and Master degrees in 2014. Pearson correlation coefficient, variance analysis test and covariance analysis employed for analyzing data. The results of the study indicate that there is not a significant difference between changes of emotional intelligence scores in accounting students with non-accounting ones and also there is no meaningful difference between students of state and non-state universities in Iran. The current study focuses on education and academic environment on emotional intelligence of accounting students which is the first study is conducted in Iran.

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)0.002
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.153
GPT teacher head0.493
Teacher spread0.341 · 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
Published2015
Admission routes1
Has abstractyes

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