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Record W2597542899

Influence of Gender and Age on Organisational Commitment Among Civil Servants in South-West, Nigeria

2017· article· en· W2597542899 on OpenAlexvenueno aff
Modupe Olayinka Ajayi

Bibliographic record

VenueCanadian social science · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsCivil servantsCivil serviceRemunerationGovernment (linguistics)Work (physics)Service (business)Age groupsVariance (accounting)Political sciencePsychologyPublic relationsSociologyBusinessDemographyPublic serviceMarketingLawEngineeringPoliticsAccounting
DOInot available

Abstract

fetched live from OpenAlex

The paper examines the influence of demographic variables of gender and age on the commitment of employees in the Nigerian civil service. Data for the study were obtained through 567 valid questionnaire containing information on gender and age, and work related issues from civil servants purposively selected from six states in the South-West, Nigeria. The Analysis of Variance (ANOVA) was used for the data. Findings indicate that the age groups of the civil servants are critical to their commitment in the organisation. The findings indicated that commitments in the civil service organisation are higher for the younger and older civil servants than those within the middle age groups. The government is provided with information on what can be done to enhance employees’ commitment to the Nigerian civil service through adequate remuneration and motivation for the different age groups. The paper concludes that civil servants should be motivated according to the needs of the age groups in order to enhance their commitment levels.

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 score0.059
Threshold uncertainty score0.991

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 studies0.0010.000
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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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