Inovasi Pelayanan Konsultasi dan Pengaduan Administrasi Kependudukan Secara Terpadu (D’Trust) di Dinas Kependudukan dan Catatan Sipil Kota Pinrang
Bibliographic record
Abstract
Public demand for excellent service forces the government to make various innovations. Innovation is one of the solutions in realizing good government services such as those carried out by the Population and Civil Registration Office of Pinrang City which created an integrated population administration consultation and complaint service innovation (D'Trust) designed to overcome problems in resolving complaints and lack of access to information. The research method used is descriptive qualitative research. The theory used in this research is the theory of innovation attributes proposed by Everett M. Rogers in Suwarno (2008) which consists of 5 (five) indicators, namely relative advantage, compatibility, complexity, triability, and observability. Data collection techniques such as interviews, documentation and observation. The results of the study prove that the D'Trust innovation launched by the Population and Civil Registration Office of Pinrang City has run well and optimally because it can facilitate the work of employees in managing community complaints in a systemized and integrated manner. In terms of benefits and service processes, it becomes more effective and efficient because public complaints can be made directly and online which are received by officers who then input, classify, analyze and provide solutions to these complaints according to the intended administrative services. The D'Trust platform is user-friendly enough to be operated by officers although initially it requires adaptation to the new technology.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".