REAKTUALISASI UNDANG-UNDANG JABATAN NOTARIS TERKAIT DIGITALISASI MINUTA AKTA OLEH NOTARIS
Bibliographic record
Abstract
Notary deed can be used as written evidence in court (civil and criminal).The notary then saves the deed as a million deed which is part of the notary protocol.The principle of prudence is needed by the Notary in saving the deed of minuta until the Notary retires.However, the reality is that the minutta deed is often scattered because of many things, such as changing positions, lack of employee responsibility, or coercing majors.In fact, UUJN does not regulate the completion of damaged or missing minuta.The purpose of this paper is to determine the position of the notary deed as evidence (civil and criminal) and analyze the need for the actualization of UUJN related to digitalization of the deed of minutes by the notary public.Research results and conclusions are; First, the position of the notary deed as evidence in criminal cases (only limited to the strength of evidence) and civil (strength of birth, formal, and material evidence).Second, the need for the actualization of UUJN related to digitalization of notary deed by a notary because it has economic and legal benefits.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".