PENGARUH PELATIHAN PENGEMBANGAN DAN KARAKTERISTIK INDIVIDU TERHADAP KINERJA PELAYANAN DINAS KEPENDUDUKAN DAN PENCATATAN SIPIL KAB.TIMOR TENGAH SELATAN
Bibliographic record
Abstract
Penelitian ini dilakukan di Dinas Kependudukan dan Pencatatan Sipil Kabupaten Timor Tengah Selatan. Fenomena yang terjadi adalah kurangnya tenaga operasional dalam melakukan pekerjaan baru dan kurangnya ketepatan waktu peneyelesaian pekerjaan. Hal ini mungkin dipengaruhi oleh kurangnya program pelatihan pengembangan dan Karaktristik Individu yang ada di Dinas Kependudukan dan Pencatatan Sipil Kabupaten Timor Tengah Selatan.Metode penelitian ini menggunakan metode deskriptif dan asosiatif. Unit analisis dalam penelitian ini adalah seluruh karyawan dan UPT Dina Kependudukan dan Pencatatan Sipil Kabupaten Timor Tengah Selatan yang berjumlah 58 orang. Teknik sampling yang digunakan adalah sampling jenuh atau sensus.Hasil penelitian ini diketahui bahwa pelatihan pengembangan berpengaruh terhadap kinerja pelayanan sebesar 45,9%, sementara karakteristik individu berpengaruh terhadap kinerja pelayanan sebesar 31,1%. Sedangkan pengaruh secara simultan antara pelatihan pengembangan dan karakteristik individu terhadap kinerja pelayanan sebesar 44,7%.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".