Legal Pluralism in Indonesia \nBridging the unbridgeable
Bibliographic record
Abstract
Writing a book is really not a solitary undertaking. It involves dialogue, debate, \nand rejoinder between myself and the many people surrounding me. In the course \nof working on this book, I have been very fortunate to benefit from the help of \nmany scholars, friends, and colleagues. The majority of this book comes from the \ndoctoral thesis I submitted to the Faculty of Law, McGill University Montreal, \nCanada, in 2006. A lot of work was, however, undertaken afterwards to incorporate \nsome new data, especially from the many aspects oflaw and legal system in \nIndonesia which has experienced so much change since my return to my home \ncountry after studying abroad. \nI have argued with and learned from my friends, colleagues, and fellow \nIndonesians as well as Indonesianists in the course of completing the manuscript. \nDuring my stay in Montreal for my doctoral studies I worked with many friends \nwho were also committed and interested in developing and improving Indonesian \nsociety. Our friendship has not only helped me to develop myself as an academic \nbut has also strengthened my commitment to social activism. During my field \nresearch in Indonesia, I owed so much to the help and support of many staff and \njudges in a number of courts and offices, including: a number of judges in the \nSupreme Court in Jakarta, Religious Courts and General Courts in the province \nofDaerah Istimewa Yogyakarta, as well as the staff of the Institute for National \nLaw Development (Badan Pembangunan Hukum Nasional), the Ministry of \nReligious Affairs, and the national library in Jakarta, whose names I cannot \nmention one by one.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".