Who Is in Justice? Caste, Religion and Gender in the Courts of Bihar over a Decade
Bibliographic record
Abstract
Bihar is widely regarded as one of India’s poorest and most divided states. It has also been the site of many social movements that have left indelible marks on the state’s politics and identity. Little is currently known about how structural inequalities have affected the functioning of formal systems of justice in the state. This paper uses a novel dataset of more than one million cases filed at the Patna high court between 2009 and 2019 together with a variety of supplementary data to analyze the role of religion, caste and gender in the high court of Bihar. The analysis finds that the courts are not representative of the Bihari population. Muslims, women and scheduled castes are consistently under-represented. The practice of using “caste neutral†names is on the rise. Though there is little evidence of “matching†between judges and petitioners or judges and filing advocates on the basis of names, there is evidence that petitioners and their advocates match on the basis of identity such as the use of “caste neutral†names. These results suggest that the social movements that disrupted existing social structures in the past may have inadvertently created new social categories that reinforce networks and inequalities in the formal justice system.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".