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Record W7042404642

PENGARUH DISIPLIN KERJA, LINGKUNGAN KERJA DAN PRODUKTIVITAS TERHADAP KINERJA KARYAWAN PADA BENGKEL ROFI MOTOR

2023· dissertation· id· W7042404642 on OpenAlexaff

Bibliographic record

VenueStiesia Repositories (Sekolah Tinggi Ilmu Ekonomi Indonesia) · 2023
Typedissertation
Languageid
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsSampling (signal processing)Data samplingQualitative analysis
DOInot available

Abstract

fetched live from OpenAlex

mengetahui dan menganalisis pengaruh disiplin kerja, lingkungan kerja dan produktivitas terhadap kinerja karyawan Bengkel Rofi Motor. Penelitian yang telah digunakan di dalam penelitian ini yaitu kuantitatif. Populasi yang digunakan di dalam penelitian ini adalah karyawan Bengkel Rofi Motor. Data yang digunakan yaitu data primer. Pengambilan sampel yang telah digunakan di dalam penelitian ini dengan sampling jenuh. Pengumpulan data digunakan dengan penyebaran kuesioner dengan jumlah sampel sebanyak 40 responden. Analisis yang digunakan di dalam penelitian ini adalah teknik analisis regresi linear berganda, dengan menggunakan alat bantu SPSS (Statistical Product and Service Solution) versi 23.0. Hasil penelitian menunjukkan bahwa disiplin kerja berpengaruh positif dan signifikan terhadap kinerja karyawan, lingkungan kerja berpengaruh positif dan signifikan terhadap kinerja karyawan, dan produktivitas berpengaruh positif dan signifikan terhadap kinerja karyawan.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.058
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0580.015

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 head0.018
GPT teacher head0.280
Teacher spread0.262 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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