Inside Indonesia’s Religious Courts: An argument for domestic and family violence screening and exemption from compulsory mediation
Bibliographic record
Abstract
ABSTRACT: In Indonesia, Islamic norms have been codified into State family law and divorce petitions are handled by State ‘Religious Courts’ (Pengadilan Agama). Formal, court-annexed mediation at the Religious Courts exists alongside customary (adat) mediation at the community level. In cases involving domestic and family violence, victims are socially expected to mediate at the community level before proceeding to the Religious Courts for formal determination of divorce petitions. At the Religious Courts, mediation is currently compulsory for all divorce petitions, even where there is proof or allegations of domestic and family violence. We argue that mediation should not be compulsory for victims of domestic and family violence who seek divorce via State family law avenues. In practice, victims bringing divorce petitions to the courts have generally already participated in mediation at the community level and therefore, further mediation has little—if any—utility. Further, there are well-documented risks for victims of domestic and family violence in mediation contexts. We draw on the comparative models of family violence screening from Australia and Canada to argue that similar screening processes should be introduced into Indonesia’s Religious Courts in order to exempt such cases of violence from compulsory mediation.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 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".