PENGARUH HUMAN CAPITAL, PENGEMBANGAN KARIR DAN MOTIVASI KERJA TERHADAP KINERJA PEGAWAI PADA KANTOR WILAYAH KEMENTRIAN HUKUM DAN HAK ASASI MANUSIA SULAWESI SELATAN
Bibliographic record
Abstract
Penelitian ini bertujuan untuk menganalisis pengaruh human capital, pengembangan karir, dan motivasi kerja terhadap kinerja pegawai Aparatur Sipil Negara (ASN) di Kantor Wilayah Kementerian Hukum dan Hak Asasi Manusia Sulawesi Selatan. Metode yang digunakan adalah pendekatan kuantitatif dengan desain korelasional. Data dikumpulkan menggunakan kuesioner dan dianalisis dengan regresi linier berganda. Hasil penelitian menunjukkan bahwa secara parsial, ketiga variabel berpengaruh positif dan signifikan terhadap kinerja pegawai. Secara simultan, human capital, pengembangan karir, dan motivasi kerja juga menunjukkan pengaruh positif yang signifikan. Dari analisis, motivasi kerja tercatat sebagai variabel yang paling dominan mempengaruhi kinerja pegawai. Temuan ini mengindikasikan bahwa meningkatnya motivasi kerja adalah kunci untuk meningkatkan kinerja ASN. This research aims to analyze the influence of human capital, career development, and work motivation on the performance of State Civil Apparatus (ASN) employees at the Regional Office of the Ministry of Law and Human Rights of South Sulawesi. The method used is a quantitative approach with a correlational design. Data was collected using questionnaires and analyzed through multiple linear regression. The research results indicated that partially, all three variables have a positive and significant effect on employee performance. Simultaneously, human capital, career development, and work motivation also demonstrate a positive and significant impact. Analysis shows that work motivation is the most dominant variable influencing employee performance. These findings suggest that enhancing work motivation is key to improving ASN performance.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".