Addressing Criminal Legal System Health Inequities Through Redirection
Bibliographic record
Abstract
The criminal legal system and youth delinquent systems (CL/DS) are increasingly recognized as a primary sources of racial/ethnic inequity (REI) in health. Research funding institutions are beginning to focus on the CL/DS as a social determinant of health, some CL/DS officials are centering equity as a core concern, and some problematic CL/DS practices are being rolled back. However, the impact of such efforts may be limited because they typically focus on the most visible manifestations of inequity but often fail to address root causes and complex system dynamics. More meaningfully reducing CL/DS-linked health inequities requires a better understanding of their complexity and barriers to change. Preparatory work is needed to develop appropriate information, communication strategies, and collaborative partnerships. To support these arguments, we offer an evaluation of our ongoing qualitative and quantitative research in New Mexico, which focuses on practices that redirect individuals from typical CL/DS practices in ways that minimize CL/DS involvement and its consequences. We review the sources of REI in CL/DS redirection practices, discuss how increased collaboration between public health and public safety agencies may impact REI, describe how differences in values, priorities, and strategies toward issues of equity pose barriers to REI reduction efforts, and discuss important building blocks for reducing siloing and working toward greater health equity through redirection from the CL/DS.
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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 | 0.017 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".