Budaya dan Kewarganegaraan : Identitas Ganda dalam Masyarakat Multikultural Indonesia
Bibliographic record
Abstract
This study investigates the role of citizenship in the context of dual identity in Indonesia's multicultural society, as well as how Islamic law and national law interact with law enforcement. Due to the extraordinary cultural and religious diversity, Indonesia faces challenges in integrating the dual identities of its people. Cultural and religious identities often lead to conflicts among citizens governed by national law, especially when it comes to religious rules and Islamic law. How dual identity affects law enforcement in Indonesia is explored through qualitative research using descriptive-analytical techniques. Data was obtained through in-depth interviews with relevant individuals and analysis of national legal and religious documents. The research results indicate that although Law No. 12 of 2006 on Citizenship provides a legal basis for all citizens. In reality, Islamic law often plays a larger role in the lives of Muslim communities. The disagreement between religious law and national law causes chaos in law enforcement, especially regardingin heritance and family issues. In addition, this research examines the legal systems in other countries with multicultural populations, such as Malaysia, India, and Canada. These countries demonstrate similar issues in creating fair legal policies for multicultural societies. This research concludes that an inclusive approach must be used in the formulation of legal policies in Indonesia so that dual identities in society can be accepted by the state’s legal system, allowing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".