PENGARUH PENGALAMAN KERJA, KOMPETENSI DAN MOTIVASI KERJA TERHADAP KINERJA APARATUR SIPIL NEGruh Pengalaman Kerja, Kompetensi dan Motivasi Kerja terhadap Kinerja Aparatur Sipil Negara pada Kantor Kecamatan Maritengngae Kabupaten Sidenreng Rappang
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui dan menganalisis (1) pengaruh pengalaman kerja, kompetensi dan motivasi kerja secara parsial dan simultan terhadap kinerja Aparatur Sipil Negara pada Kantor Kecamatan Maritengngae Kabupaten Sidenreng Rappang (2) variabel yang paling dominan berpengaruh terhadap kinerja Aparatur Sipil Negara pada Kantor Kecamatan Maritengngae Kabupaten Sidenreng Rappang. Metode pengumpulan data yang digunakan adalah angket dan studi dokumen. Metode analisis yang digunakan adalah analisis statistik deskriptif dan analisis regresi linear berganda. Hasil penelitian menunjukkan bahwa (1) secara parsial pengalaman kerja, kompetensi, dan motivasi berpengaruh positif dan signifikan terhadap kinerja Aparatur Sipil Negara pada Kantor Kecamatan Maritengngae Kabupaten Sidenreng Rappang (2) secara simultan pengalaman kerja, kompetensi dan motivasi kerja berpengaruh positif dan signifikan terhadap kinerja pegawai Kecamatan Maritengngae Kabupaten Sidenreng Rappang. Hal ini berarti semakin baik pengalaman kerja, kompetensi dan motivasi kerja yang dimiliki oleh pegawai maka kinerja pegawai akan semakin baik pula (3) variabel kompetensi memiliki pengaruh paling dominan dan signifikan terhadap kinerja pegawai di kecamatan Maritengngae Kabupaten Sidenreng Rappang.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".