Balancing Regulatory Efficiency and Halal Integrity:A Governance Analysis of Indonesia's Self-Declaration System in Halal Certification
Bibliographic record
Abstract
Indonesia’s halal certification self-declaration system faces criticism for oversight and accountability gaps. This study evaluates its compliance with transparency principles under national law and Islamic jurisprudence (fatwa), employing a mixed-methods analysis of legislative texts, case studies, and interviews. Contrasting law in books and law in action, findings reveal systemic gaps between regulatory ideals and implementation. Procedural misconduct—including data falsification and lax verification by Halal Product Process (PPH) officers—undermines accountability, exposing flaws in coordination and audit mechanisms. The self-declaration model inadequately safeguards halal integrity, requiring reforms: real-time monitoring, standardized verification protocols, and collaborative governance between Indonesia’s National Ulama Council (MUI) and state agencies. Such measures are vital to aligning Indonesia’s halal assurance with global Islamic economy benchmarks while safeguarding consumer trust and principles. The study underscores bridging normative-practical divides through adaptive governance, ensuring coherence and compliance in Indonesia’s evolving halal ecosystem.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".