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Record W2513943193 · doi:10.6000/1927-5129.2016.12.53

Associations of Intellectual Ability with Emotional Intelligence, Academic Achievement and Aggression of Adolescents

2016· article· en· W2513943193 on OpenAlexvenueno aff
Mehwish Mursaleen, Seema Munaf

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2016
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsEmotional intelligencePsychologyAggressionAcademic achievementDevelopmental psychologyTest (biology)Intelligence quotientScale (ratio)Clinical psychologyCognitionPsychiatry

Abstract

fetched live from OpenAlex

Purpose: The current study examined the relationship of intellectual ability with emotional intelligence, academic achievement, and aggression of adolescents.Methods: Correlational design was utilized to study the relationship between these variables. Adolescent students (N=500, 50% boys & 50% girls, with the mean age of 15.01 years & SD of 1.11) were approached from different private schools and colleges of Karachi. To measure their intellectual ability, emotional intelligence, and aggression, Draw-A-Person Intellectual Ability Test for children, adolescents, and adults (DAP: IQ), Wong and Law Emotional Intelligence Scale (WLEIS), and Aggression Questionnaire-Short Form (AQ-12) were administered. Their academic achievement was assessed through their percentage of most recent examination. Pearson product moment correlation coefficient was utilized to analyse the results.Results: Intellectual ability was significantly positively related with emotional intelligence and its domains i.e. Self-Emotional Appraisal, Others’ Emotional Appraisal, Use of Emotions, and Regulation of Emotions (p

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.393

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.064
GPT teacher head0.352
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 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

Citations4
Published2016
Admission routes1
Has abstractyes

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