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Gender and City Differences in Personality Traits among Adolescents in Some Selected Cities of Nigeria

2013· article· en· W1546271371 on OpenAlexvenueno aff
Charles C. Nweke, Charles O. Anazonwu, Rita Ugokwe-Ossai, Valentine Ucheagwu

Bibliographic record

VenueCross-cultural communication · 2013
Typearticle
Languageen
FieldPsychology
TopicPersonality Traits and Psychology
Canadian institutionsnot available
Fundersnot available
KeywordsNeuroticismBig Five personality traitsPersonalityPsychologyPersonality psychologyTraitDemographyPearson product-moment correlation coefficientDevelopmental psychologyClinical psychologySocial psychologySociology

Abstract

fetched live from OpenAlex

There are overwhelming evidences from researches in the regional science that the attitudes, values and behaviours of people are geographically clustered. Psychologists, however, have historically had little to say about regional and city differences (Rentfrow, 2010). The present study investigated on gender and city differences in trait personalities among adolescents in some selected cities within Nigeria. A thousand and one (1001) adolescents (532 females, 469 males) sampled from five cities (Markurdi, Calabar, Nnewi, Victoria Island, Benin) within Nigeria were employed for the study. Big Five Personality Inventory by John, Donahue and Kentle (1991) was used to gather their data on personality traits while Multivariate Analysis of Covariance (MANCOVA) and Pearson Product Moment correlation were used in data analysis. The findings of the study showed significant city difference on the personality traits examined and significant gender differences on neuroticism personality. Similarly significant interaction effects of city and gender were also seen. Furthermore there were positive and negative correlations of age and personality among adolescents studied. Discussions of the findings were done as well as the implications of the findings for social behaviours.

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)0.000
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 score0.016
Threshold uncertainty score0.684

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.068
GPT teacher head0.358
Teacher spread0.290 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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