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Perception of Civil Servants on the Knowledge of Integrity and Corruction Level in Three State Agencies in Terengganu, Malaysia

2010· article· en· W1942129305 on OpenAlexvenueno aff
Iskandar Hasan Tan Abdullah, Asri Salleh, Rusnah Ismail, Nazlin Emieza Ngah

Bibliographic record

VenueCanadian social science · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCivil servantsLanguage changeCivil servantPerceptionCivil servicePolitical sciencePublic sectorComprehensionNonprobability samplingPublic administrationPublic servicePublic relationsManagementPsychologySociologyLawEconomicsComputer science

Abstract

fetched live from OpenAlex

Malaysia in its effort to become a fully developed nation by 2020 has undertaken a monumental task in instilling noble values into its society including its civil sector. The objective of this research was to examine the level of awareness, knowledge and understanding of integrity among selected civil servants of three State agencies in Terenganu, Malaysia, which basically served as the scope of the research. The respondents were selected based on a purposive sampling technique. The research used a quantitative approach where self-administered structured questions were distributed. Descriptive and inference analysis were carried out to meet the research objectives outlined. The research findings indicated that there were civil servants groups who were unclear and uncertain about the meaning of integrity in public sector. Those with shorter length of services were found to be less knowledgeable on the respective rule and procedure which led to them being unsure about integrity. The civil servants also believed that corruption level in Malaysia was still high. The research contended that length of service was inversely related to the perception on the knowledge of integrity, corruption and quality of service delivery.Keywords: Integrity; Civil Servants; Perception; Corruption; Public Sector; Public Service Resume: Dans l'effort de devenir une nation pleinement developpee d'ici a 2020, la Malaisie a entrepris une tâche monumentale pour inculquer des valeurs nobles dans la societe, y compris dans le secteur civil. L'objectif de cette recherche etait d'examiner le niveau de sensibilisation, de connaissances et de comprehension de l'integrite des fonctionnaires selectionnes des trois organismes d'Etat a Terenganu en Malaisie, qui a essentiellement servi a la portee de la recherche. Les repondants ont ete selectionnes sur la base d'une technique d'echantillonnage raisonnee. La recherche a utilise une approche quantitative ou des questions auto-administrees et structurees ont ete distribuees. Des analyses descriptives et inferees ont ete realisees pour repondre aux objectifs enonces de recherche. Les resultats de la recherche a indique qu'il y avait des groupes de fonctionnaires civils qui n'etaient pas claires sur le sens de l'integrite dans le secteur public. Ceux avec une plus courte duree de services ont ete trouves d'etre moins bien informes sur la regle respective et la procedure, ce qui les a donne un doute sur l'integrite. Les fonctionnaires ont egalement estime que le niveau de corruption en Malaisie etait toujours eleve. La recherche a fait valoir que la duree de service etait liee inversement a la perception de la connaissance de l'integrite, de la corruption et de la qualite de prestation des services.Mots-cles: integrite; fonctionnaires; perception; corruption; secteur prive; service public

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.245
Teacher spread0.211 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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