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Record W3042115545 · doi:10.33701/jmsda.v6i2.496

PENGARUH KEPEMIMPINAN, PENGEMBANGAN SUMBER DAYA MANUSIA DAN KEPUASAN KERJA TERHADAP KINERJA PEGAWAI DI KECAMATAN CILEUNYI, KABUPATEN BANDUNG, PROVINSI JAWA BARAT

2018· article· en· W3042115545 on OpenAlexaff
Ayu Ambarawati

Bibliographic record

VenueJurnal MSDA (Manajemen Sumber Daya Aparatur) · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsJob satisfactionHuman resourcesJavaPsychologyRegression analysisBusiness administrationManagementStatisticsAgricultural scienceBusinessOperations managementMathematicsEngineeringSocial psychologyComputer scienceOperating systemEconomicsBiology

Abstract

fetched live from OpenAlex

ABSTRACT In measuring performance based on leadership, development and job satisfaction in Cileunyi District, Bandung Regency, West Java. experienced several obstacles. Based on this background the author takes the title: The Effect of Leadership, Human Resource Development and Job Satisfaction on Employee Performance of Cileunyi District Offices, Bandung Regency, West Java. “ The purpose of this study was to determine the effect of leadership, human resource development and job satisfaction on performance in Cileunyi District, Bandung Regency, West Java. both partially and simultaneously. Partially Leadership Variables (X1) have a positive and significant effect on employee performance. This is indicated by the regression coefficient of 0.233. Whereas for tcount (3,953)> t table (1,697) and sign. (0,001) t table (1,697) and sign (0,014) t table (1.697) and sign. (0.000) Ftable (2,98 and sign. (0,000)

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)0.000
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 score0.015
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.002

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 head0.025
GPT teacher head0.300
Teacher spread0.276 · 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
Published2018
Admission routes1
Has abstractyes

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