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Record W4379912091 · doi:10.29303/oportunitas.v2i1.476

PENGARUH DISIPLIN, MOTIVASI KERJA DAN BUDAYA KERJA TERHADAP KUALITAS PELAYANAN DINAS KEARSIPAN DAN PERPUSTAKAAN KOTA MATARAM

2023· article· id· W4379912091 on OpenAlexaff
Isti Nurinayah, Sataruddin Sataruddin, Tuti Handayani

Bibliographic record

VenueJurnal Oportunitas Ekonomi Pembangunan · 2023
Typearticle
Languageid
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsPsychologyHumanitiesArt

Abstract

fetched live from OpenAlex

Penelitian ini dilakukan bertujuan untuk menganalisis Pengaruh disiplin, motivasi kerja, dan budaya kerja terhadap kualitas pelayanan Dinas Kearsipan dan Perpustakaan Kota Mataram secara parsial (mandiri) maupun secara simultan (bersama-sama). Penelitian ini merupakan penelitian kuantitatif deskriptif melalui metode sampel survei dengan tehnik pengumpulan data melalui wawancara responden dengan alat analisis kuesioner. Menggunakan dua populasi yaitu pegawai Dinas Kearsipan dan Perpustakaan Kota Mataram dan juga pengunjung Dinas Kearsipan dan Perpustakaan Kota Mataram dengan tehnik pengambilan sampel non-probability sampling yaitu accidental untuk pengunjung dengan mengambil 39 orang pengunjung sebagai responden. Sedangkan untuk pegawai Dinas Kearsipan dan Perpustakaan Kota Mataram mengambil keseluruhan populasi sebagai responden yaitu 39 orang pegawai. Pengujian hasil penelitian dengan menggunakan uji instrument penelitian uji validitas dan reabilitas, uji asumsi klasik, uji hipotesis dan analisis regresi linier berganda. Hasil dari penelitian ini menunjukan bahwa disiplin pegawai, motivasi kerja, dan budaya kerja berpengaruh dan signifikan terhadap kualitas pelayanan Dinas Kearsipan dan Perpustakaan Kota Mataram secara parsial (mandiri) maupun secara simultan (bersama-sama).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.050
GPT teacher head0.301
Teacher spread0.251 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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