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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 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)0.003
metaresearch head score (Gemma)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.003
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.001
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 head0.087
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; 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

Citations5
Published2019
Admission routes2
Has abstractyes

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