Assessing the Impact of Cannabis Decriminalization on Racial Disparities in Chicago’s Cannabis Possession Arrests
Bibliographic record
Abstract
Black and Hispanic neighborhoods have suffered the most severe consequences of the “war on drugs.” As the war on drugs waned, cannabis legalization/decriminalization efforts increased across America. A prime example of decriminalization occurred in August of 2012 as the City of Chicago introduced a new law providing officers with option to ticket, rather than arrest, individuals caught in possession of 15 grams of cannabis or less. As cannabis policy continues evolving, it remains to be seen whether or not the trend toward decriminalization will produce equitable changes in drug arrest outcomes across racial/ethnic groups. We employ data tracking cannabis arrests over time by neighborhood to assess the impact of cannabis decriminalization in Chicago and estimate racial disparities in the likelihood of arrest (v. ticket) using two sets of models: within-neighborhood models and hierarchical logistic regressions with random effects. We find that Blacks and non-White Hispanics are more likely to be arrested than ticketed for minor cannabis possession in Chicago following the introduction of the Alternative Cannabis Enforcement (ACE) program, regardless of the neighborhood where the arrest took place. In addition, Black neighborhoods did not experience the same reduction in arrests after the law changed in comparison with racially mixed, White, or predominantly Hispanic neighborhoods. Our findings draw attention to the differential deployment of discretionary policing strategies across neighborhoods of different racial/ethnic composition. Although Chicago’s ACE program has lowered the overall rate of cannabis arrests, major racial/ethnic disparities in those arrests remain and become exacerbated when examining macro neighborhood-level trends.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".