Socio-Legal Approach in Land Acquisition Implementation for Public Interests: A Case Study of The Construction of A Connecting Bridge Batulicin – Kotabaru
Bibliographic record
Abstract
This study examines the socio-legal approach to land acquisition for public purposes, focusing on the construction of the Batulicin-Kotabaru Bridge. Land acquisition is often a crucial point in infrastructure development, where legal norms interact with the social realities of the community. This qualitative study uses a socio-legal approach to analyze how the legal framework (Law No. 2 of 2012) is implemented in the field and how social dynamics—such as community perceptions, compensation issues, and deliberation processes—influence the project's success. Primary data was obtained through in-depth interviews with government officials, affected communities, and academics, while secondary data was collected from official documents and related literature. The results show that a socio-legal approach that integrates strong legal aspects with a deep understanding of the community's social conditions is highly effective in minimizing disputes. Active community involvement from the early stages, a transparent deliberation process (FGD), and the determination of fair and appropriate compensation values are key to success. This study recommends strengthening mediation mechanisms and proactive communication to ensure community rights are fulfilled, thereby enabling the achievement of development goals without causing social conflict.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".