Increasing Pretrial Releases and Reducing Felony Convictions for Defendants: Implications for Desistance from Crime
Bibliographic record
Abstract
The bulk of the desistance literature has focused on social/contextual factors (marriage, employment, peers) and their criminogenic consequences. Less attention has been devoted to the role of criminal justice system involvement in the desistance process, and most of the existing research indicates that system involvement tends to inhibit or delay desistance from crime. One recent effort to combat that pattern was implemented with the Responsive Interventions for Change (RIC) Docket in Harris County, Texas, in 2016. The RIC Docket was intended to increase defendants’ access to a pretrial release bond and to reduce rates of felony convictions, thus lowering the risk of disrupting important prosocial ties and avoiding potentially stigmatizing labels. In the present study, we use case processing data on rates of pretrial release and felony convictions from one year prior to (N = 6,792) and three years following (N = 12,152) the implementation of the RIC Docket. Results show that those processed through the RIC Docket were 24% more likely to have access to pretrial release and 45% less likely to have their cases result in a conviction. We conclude by discussing the importance of policy changes intended to reduce barriers to the successful desistance process for individuals involved in the justice system.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".