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Record W4312417021 · doi:10.24967/dikombis.v1i4.1799

BUDAYA DAN LINGKUNGAN KERJA TERHADAP KINERJA PEGAWAI DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL

2022· article· id· W4312417021 on OpenAlexaff
Junaidi Junaidi, Hazairin Habe

Bibliographic record

VenueDikombis Jurnal Dinamika Ekonomi Manajemen dan Bisnis · 2022
Typearticle
Languageid
FieldSocial Sciences
TopicEmployee Performance and Management
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsPhilosophy

Abstract

fetched live from OpenAlex

Penelitian yang akan dilaksanakan jenis penelitian deskriptif kuantitatif. Jenis penelitian ini adalah penelitian yang bermaksud membuat pemaparan secara sistimatis, faktual, dan akurat mengenai fakta–fakta dan sifat–sifat populasi tertentu. Jenis penelitian yang digunakan dalam penelitian ini adalah penelitian lapangan (field reseach), bersifat kuantitatif dimana penjelasannya bersifat objektif dengan menjelaskan pendekatan- pendekatan yang ada. Tujuan dari penelitian ini adalah mengetahui pengaruh budaya dan lingkungan kerja terhadap kinerja pegawai di Dinas Kependudukan dan Pencatatan Sipil Kabupaten Way Kanan. Penelitian ini bersifat kuantitatif di mana penjelasannya bersifat objektif dengan pemaparan secara sistimatis, akurat, dan faktual mengenai fakta-fakta dan sifat-sifat populasi yang diteliti dengan melakukan analisis data statistika menggunakan software aplikasi SPSS Version 26. Penelitian ini menggunakan 27 responden penelitian. Dari hasil analisis pengolahan data dapat disimpulkan bahwa ada pengaruh secara parsial (sendiri-sendiri) dan secara simultan (bersama-sama) dari variabel Budaya dan Lingkungan Kerja terhadap Kinerja Pegawai pada Dinas Kependudukan dan Pencatatan Sipil Kabupaten Way Kanan Kata kunci: Budaya Kerja, Lingkungan Kerja, Kinerja Pegawai.

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: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0290.005

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 head0.023
GPT teacher head0.272
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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