Pulling Back the Curtain on the California Gang Database: Evidence of Racial, Ethnic and Gender Disparities Among 222 Law Enforcement Agencies
Bibliographic record
Abstract
Abstract The California Gang Database (CalGang) is the first, largest and arguably most controversial shared gang database in the United States. This study examined its demographic composition and disparities in 103,840 records input by 222 unique law enforcement agencies between 2017 and 2022; the database was 94 per cent male, 66 per cent Hispanic, 23 per cent Black, 51 per cent 18 to 30 years old and 38 per cent 31–45 years old. About one-quarter of 1 per cent of Californians are listed in CalGang. Age-standardized estimates indicated that males were overrepresented relative to females by a factor of 17 and that Black and Hispanic males were overrepresented relative to White males by factors of 33 and 11, respectively, while Asian males were underrepresented. These demographic disparities generalized across nearly all law enforcement agencies. Gang databases will remain highly controversial owing to significant racial/ethnic disparities, but also concerns about civil liberties, due process, privacy rights and collateral consequences. The generative questions that remain are whether the observed disparities can be explained by legal factors and whether any public safety value can be achieved while protecting individual rights.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".