Pelatihan Digitalisasi Data Pertanahan bagi Pemerintah Kalurahan Pampang, Kapanewon Paliyan, Kabupaten Gunungkidul
Bibliographic record
Abstract
Pemerintah Kalurahan mempunyai peran penting dalam membantu dan melayani administrasi pertanahan tetapi masih menghadapi kendala digitalisasi data. Data-data yang terdapat di Pemerintah Kalurahan masih bentuk cetak dan disimpan dengan cara manual. Data yang dimiliki oleh Pemerintah Kalurahan tidak pernah diperbarui. Permasalahan ini yang menjadi dasar program pemberdayaan masyarakat yang bertujuan untuk meningkatkan kapasitas aparatur Kalurahan Pampang dalam mengelola data pertanahan secara digital melalui pendekatan Participatory Action Research. Kegiatan dilakukan melalui pelatihan digitalisasi data, pendampingan teknis, serta evaluasi keberhasilan program. Hasil kegiatan menunjukkan peningkatan pemahaman aparatur tentang sistem informasi pertanahan serta penerapan sistem digital dalam pengelolaan data. Program ini berdampak pada peningkatan kesiapan kalurahan dalam mendukung kebijakan nasional terkait pengelolaan pertanahan digital.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.057 | 0.017 |
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".