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Record W3107573456 · doi:10.29303/jmm.v9i4.582

PENGARUH BUDAYA ORGANISASI, LINGKUNGAN KERJA DAN KOMPETENSI TERHADAP KINERJA PEGAWAI DINAS PERTANIAN DAN PERKEBUNAN KABUPATEN BIMA

2020· article· en· W3107573456 on OpenAlexaff
Fauzi Muhammad Nur, Siti Nurmayanti, Sri Tatminingsih

Bibliographic record

VenueJMM UNRAM - MASTER OF MANAGEMENT JOURNAL · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsCompetence (human resources)Civil servantsAgricultural scienceWorking environmentBusiness administrationAgricultureOrganizational cultureManagementOperations managementBusinessPsychologyEngineeringGeographyPolitical scienceEnvironmental scienceEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

This research is aimed to find out a partial and/or simultaneous influence of organizational culture, working environment, and competence of employees toward their working performance. The method used is a descriptive quantitative method using a questionnaire technique. Data were gained and collected since June to September 2019 by questionnaires distributed to 119 civil servants working in Agriculture and Plantation Services of Bima Regency. Furthermore, data were analyzed and processed using SPSSver.22 data processing tool for windows. Results of the analysis then interpreted and narrated descriptively. The results indicated that organizational culture (X1), working environment (X2), and competence (X3) as partially effect very much employees working performance (Y) in Agriculture and Plantation Services of Bima Regency. It shows the value of the t-test results for each variable, namely X1: 4.779> t table 1.98; X2: 3,327> t table 1,98; and X3: 6,207> table 1.98.Keywords: organizational culture, working environment, competence, and performance

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)0.001
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 score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.002

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 head0.039
GPT teacher head0.272
Teacher spread0.234 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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Same venueJMM UNRAM - MASTER OF MANAGEMENT JOURNALSame topicEmployee Performance and ManagementFrench-language works237,207