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Record W2923630795 · doi:10.5539/ibr.v12n4p76

Competency Management in the Context of Omani Civil Service Reform & Development

2019· article· en· W2923630795 on OpenAlexvenueaboutno aff
Ahmed Albalushi, Ashraf Mohammed Zaidan, Fakhrul Adabi Bin Abdul Khadir, Muhammed Yusof

Bibliographic record

VenueInternational Business Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsBureaucracyCivil serviceContext (archaeology)BusinessService (business)Public sectorPublic serviceCivil societyOrder (exchange)Process managementPublic administrationOperations managementPolitical scienceMarketingEngineeringFinancePoliticsGeography

Abstract

fetched live from OpenAlex

This paper aims to discuss the extent of the application-based management efficiency in the Sultanate of Oman Civil Service, by comparison with the practices and experiences of five systems of the civil service or the public in each of the (United States of America, Canada, South Korea, Saudi Arabia, United Arab Emirates). Several variables and address are necessary to achieve reform and development in the civil service such as the situation organizational and strategic system competencies, selection and appointment and based on efficiency, performance evaluation based on efficiency as one of the main functions of human resources management in the public sector, and the framework or efficiency model. In order to become a civil service in Amman of the best practices in the efficient management system at the regional and international level, providing more than ten developmental proposals paper to raise the level of the civil service, because the competency's management of important topics in the development of civil service performance, seen as a tool to shift from the traditional bureaucracy to modern organizations.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.087
GPT teacher head0.395
Teacher spread0.308 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2019
Admission routes2
Has abstractyes

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