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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.002 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it