Beyond Legal Moralism: Reconstructing Rational Justice through Economic Analysis of Law in Indonesia’s Criminal Policy
Bibliographic record
Abstract
This article critically interrogates the enduring dominance of the legal – moralistic paradigm in Indonesia’s legal system and advances the Economic Analysis of Law (EAL) as a rational and context – sensitive framework for legal reform. Departing from the premise that moralistic legal reasoning, when detached from empirical evaluation and incentive structures, often produces overcriminalization, regulatory inefficiency, and declining public trust, this study argues that EAL offers a systematic methodology to realign law with rational justice. Employing a multidisciplinary approach that integrates philosophical reflection, socio – legal analysis, and doctrinal examination, the article demonstrates that EAL is not antithetical to Indonesia’s constitutional identity or normative foundations, including Pancasila, but can instead function as an instrumental extension of its substantive justice values. By incorporating cost – benefit analysis, behavioral incentives, and evidence – based policy evaluation into legal decision – making, EAL enhances legislative rationality, optimizes enforcement mechanisms, and strengthens institutional legitimacy. Comparative insights drawn from selected jurisdictions, particularly the United Kingdom, Australia, South Korea, and Canada, illustrate how EAL has been institutionalized through Regulatory Impact Assessments, restorative justice frameworks, and interdisciplinary legal education. The article concludes by proposing a contextualized reform strategy for integrating EAL into Indonesia’s pluralistic legal culture, positioning rational justice as a mediating paradigm between moral aspiration, empirical governance, and democratic accountability.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".