Evidence of Caste-Class Discrimination from a Conjoint Analysis of Law Enforcement Officers
Bibliographic record
Abstract
When choosing what cases to investigate, do the police discriminate on the basis of caste and class? We employ a conjoint design to evaluate biases in police officers’ preferences for investigation based on perpetrator attributes. Conducting a survey of law enforcement officers in Nepal, we find evidence of discriminatory investigation practices. Absent constraining protocols that reduce officer discretion, police officers are more likely to target offenders who are from caste-class subjugated communities. Additionally, police officers’ assessments of institutional investigatory preferences reveal caste-based considerations: officers believe the police, in general, prefer to investigate low-caste offenders over high-caste offenders. They do not, however, perceive their institution as having class-based biases. These findings add to the body of evidence on whether police discriminate, which has previously focused on use of lethal force and police stops, and further demonstrate that concerns over systemic bias in policing are warranted.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".