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The effects of low-intensity supervision for lower-risk probationers: updated results from a randomized controlled trial

机译：低强度监督对低风险缓刑者的影响：随机对照试验的最新结果

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This paper explores the effects of reduced supervision intensity for probationers who were identified, using a random forest forecasting model, as presenting a low risk of committing new serious offenses. It expands on previously reported results of the Philadelphia Low Intensity Community Supervision Experiment, a randomized controlled trial performed from 2007 through 2008. We update our previous one-year recidivism results to include 18 months of follow-up data, and assess additional measures that were not available in earlier analyses, including drug-testing results, officer contact compliance, probation violations, and absconding from supervision. The updated analysis affirms previous findings, showing that reduced supervision intensity does not increase the prevalence or frequency of new offending by low-risk probationers, and does not appear to result in any additional threats to public safety. We conclude that low-intensity supervision, when used in concert with valid and reliable risk forecasting, offers community supervision agencies a powerful tool for managing large offender populations, allowing the agencies to focus scarce resources on higher-risk offenders and perhaps reduce administrative costs. Further research is needed to quantify the exact cost reductions, and to determine the best means of supervising offenders whose risk level makes them ineligible for low-intensity supervision.View full textDownload full textKeywordsprobation, parole, randomized controlled trials, risk forecasting, random forests, risk-based supervision, compliance, recidivismRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/0735648X.2012.679874
机译：本文探讨了使用随机森林预测模型降低被确定为缓刑犯的监督力度的影响，因为这对实施新的严重犯罪具有较低的风险。它扩展了先前报告的费城低强度社区监督实验的结果，该实验是2007年至2008年进行的随机对照试验。我们更新了之前的一年累犯结果，包括18个月的随访数据，并评估了其他措施。较早的分析中未提供这些信息，包括药物测试结果，警务人员的合规性，缓刑违规情况以及对监督的弃权。最新的分析证实了先前的发现，表明降低监管强度不会增加低风险缓刑者新犯罪的发生率或频率，也不会给公共安全带来任何其他威胁。我们得出的结论是，低强度监督与有效和可靠的风险预测相结合，可以为社区监督机构提供强大的工具来管理大量犯罪者，使机构可以将稀缺资源集中于高风险犯罪者，并可能降低管理成本。需要进行进一步的研究以量化确切的成本削减量，并确定监督风险程度使其不适合进行低强度监督的违法者的最佳方法。查看全文下载全文关键词集锦，假释，随机对照试验，风险预测，随机森林，基于风险的监督，合规性，累犯相关变量var addthis_config = {ui_cobrand：“泰勒和弗朗西斯在线”，servicescompact：“ citlikelike，netvibes，twitter，technorati，delicious，linkedin，facebook，stumbleupon，digg，google，更多”，pubid： ra-4dff56cd6bb1830b“};添加到候选列表链接永久链接http://dx.doi.org/10.1080/0735648X.2012.679874

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