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Record W2407796332 · doi:10.5539/ijef.v8n6p258

Influence Factors Internal and External Factors Motivation and Performance of Employees: Do not Stay Civil Service Policy Unit

2016· article· en· W2407796332 on OpenAlexvenueno aff
Pandi Afandi, Helwen Heri

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsnot available
Fundersnot available
KeywordsOfficerVariablesPopulationService (business)PsychologySample (material)Civil serviceJob performanceWork motivationWork (physics)Variable (mathematics)Social psychologyBusinessOperations managementPublic serviceMarketingJob satisfactionPublic relationsMathematicsEngineeringStatisticsPolitical scienceSociologyLawDemography

Abstract

fetched live from OpenAlex

This research of background that directional and effective use of labor represent key toward the make-up of officer performance so that need to District Police Public Service head to make a manage the members always enthusiastic in working and having positive in executing work.Effort roomates can be a head-to Increase the activity spirit of enthusiasm, that is by giving good motivation and job performance.Intention of this research is to know and Analyzed do internal factors variable, external factor variable and motivation have influenced the which significantly either through partial and Also simultaneously to job performance officer, and Also from internal factors variable, external factor variable and motivation is the which most having an effect on the which to job performance officer. This research is a survey verification sampling on 200 people from 236 respondend contract employee population of District Police Public Service Riau Province.This research represent research highlighting clarification relation between research variables and test hypotheses have been Formulated Previously roomates.Hereinafter technique intake of sample is used by population technique, where samples taken pursuant to SEM formula for 25 manifest variables using a minimum of 200 samples.Technique Analyzed using descriptive and quantitative analysis by applying the method of Structural Equation Modelling (SEM) with Analysis of Moment Structures (AMOS) programe.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.026
GPT teacher head0.274
Teacher spread0.249 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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Same venueInternational Journal of Economics and FinanceSame topicEmployee Performance and ManagementFrench-language works237,207