Realizing Legal Certainty in Electronic Land Certificates: A Critical Reflection on Ontario’s Legislative Model for Indonesia
Bibliographic record
Abstract
The digital transformation of land administration in Indonesia, under the framework of Government Regulation Number 18 of 2021, marks a crucial step toward modernizing public services. However, a fundamental problem of legal certainty for land rights arises from its delegative model of authority, under which essential technical regulations are issued through the Regulation of Minister of ATR/KBPN Number 3 of 2023. This reliance on a ministerial-level regulation creates potential long-term juridical and operational vulnerabilities. This research aims to critically analyze the weaknesses of this delegative model and project strategic solutions, employing a normative legal research method with a functional comparative approach. The Province of Ontario, Canada, a global pioneer, was selected as the comparative jurisdiction. The analysis reveals that Indonesia’s delegative model is exacerbated by practical challenges, including technological infrastructure gaps, a deficit in public trust, and uneven digital literacy. Conversely, Ontario’s integrative legislative model—supported by a comprehensive ecosystem of regulations at the Act of Parliament level, a mature public-private partnership, and strict access governance—has been functionally proven to achieve superior levels of legal certainty and efficiency. It is concluded that to achieve complete legal certainty, Indonesia must elevate and consolidate its legal framework into a comprehensive Bill on Electronic Land Registration, which would serve as a solid foundation for building a holistic digital ecosystem of trust.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.033 | 0.035 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".