IMPLEMENTASI KEBIJAKAN PENGANGKATAN APARATUR SIPIL NEGARA DALAM JABATAN STRUKTURAL DI KABUPATEN INDRAGIRI HILIR PROVINSI RIAU
Bibliographic record
Abstract
This study aims to describe the implementation of the policy of appointing state civil servants in structural positions and the factors that influence them in Indragiri Hilir Regency. This research uses descriptive qualitative research and data collection with in-depth interviews. The process of implementing ASN appointment in Indragiri Hilir Regency there are 5 stages including planning, announcement, registration, selection and appointment. Planning is carried out based on the proposed workforce needs and the slack of officials in the region which are forwarded to each agency. The announcement phase was carried out by the Baperjakat Team after it was discovered that the SKPD needed employees and had been scheduled beforehand. The registration and selection stage for the appointment of the State Civil Apparatus in Indragiri Hilir Regency is the core stage in the appointment process. The final stage in the process of appointing a State Civil Apparatus in a position in Indragiri Hilir Regency is the appointment stage. This stage was carried out after the ASN candidate had carried out the examination and was declared eligible to become a State Civil Apparatus.Keywords: appointment, state civil apparatus
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".