Community Court: The Research Literature
Bibliographic record
Abstract
The first community court opened in Midtown Manhattan in 1993. Focusing on quality-of-life offenses, such as drug possession, shoplifting, vandalism,and prostitution, the Midtown Community Court sought to combine punishment and help, sentencing low-level offenders to perform visible community restitution, receive on site social services, including drug treatment, counseling, and job training. There are currently more than 60 community court projects in operation worldwide. In the United States alone there are 33 while there are 17 in South Africa, 13 in England and Wales, and one each in Australia and Canada.Community courts seek to achieve a variety of goals, such as reduced crime, increased engagement between citizens and the courts, improved perceptions of neighborhood safety, greater accountability for low level,"quality-of-life" offenders, speedier and more meaningful case resolutions, and cost savings. In advancing these goals, community courts generally make greater use of community-based sanctions than traditional courts (Hakuta, Soroushian,and Kralstein, 2008; Katz, 2009; Sviridoff et al., 2000; Weidner and Davis, 2000). Among a sample of 25 community courts surveyed in 2007, 92 percent routinely use community service mandates, and 84 percent routinely use social services mandates (Karafin, 2008). This paper reviews the research literature to date about community courts. Community court studies have employed a number of different research methods, reflecting the variation in community court models.
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.017 | 0.038 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".