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 \n India’s poorest and most divided states. It has also been \n the site of many social movements that have left indelible \n marks on the state’s politics and identity. Little is \n currently known about how structural inequalities have \n affected the functioning of formal systems of justice in the \n state. This paper uses a novel dataset of more than one \n million cases filed at the Patna high court between 2009 and \n 2019 together with a variety of supplementary data to \n analyze the role of religion, caste and gender in the high \n court of Bihar. The analysis finds that the courts are not \n representative of the Bihari population. Muslims, women and \n scheduled castes are consistently under-represented. The \n practice of using “caste neutral” names is on the rise. \n Though there is little evidence of “matching” between judges \n and petitioners or judges and filing advocates on the basis \n of names, there is evidence that petitioners and their \n advocates match on the basis of identity such as the use of \n “caste neutral” names. These results suggest that the social \n movements that disrupted existing social structures in the \n past may have inadvertently created new social categories \n that reinforce networks and inequalities in the formal \n justice system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".