The Effects of Sanction Intensity on Criminal Conduct - JDAI Helpdesk

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does not contribute to the development <strong>of</strong> good practice in <strong>of</strong>fender and resource management. Furthermore, clinical decision-making is not immune to these problems either. Bridges and Steen (1998) showed that probation <strong>of</strong>ficers’ clinical judgments about the causes <strong>of</strong> <strong>of</strong>fending can be influenced by the client’s race, and may subsequently factor into case planning and sentencing recommendations. <strong>The</strong> Philadelphia APPD and the University <strong>of</strong> Pennsylvania have developed a new risk prediction model that is the focus <strong>of</strong> the present study. <strong>The</strong> following section describes the model, its contribution to the existing body <strong>of</strong> literature on risk assessment, and its use in a practical setting. <strong>The</strong> Philadelphia APPD Low Risk Model and Supervision Experiment <strong>The</strong> foundation for the Philadelphia APPD low risk prediction model and supervision experiment was laid in 2005, when APPD and the University <strong>of</strong> Pennsylvania began working together to restructure the agency’s probation supervision practice according to predicted risk <strong>of</strong> serious crime. This approach represented a change from the existing standard supervision model for all <strong>of</strong>fenders that was modified on an ad hoc basis largely according to <strong>of</strong>ficer discretion. In accordance with the risk principle, APPD’s eventual goal was to reallocate the highest risk <strong>of</strong>fenders to more intensive supervision, with a small ratio <strong>of</strong> clients to <strong>of</strong>ficers so that more time could be put into assessing and addressing those clients’ needs. In order to do this without spending scarce resources on new staff, the lowest risk <strong>of</strong>fenders in the agency needed to be assigned to large caseloads with minimal supervision. <strong>The</strong> first step in this process was to create a 137
statistical model that would predict the risk <strong>of</strong> serious re<strong>of</strong>fending 1 so that the whole APPD population could be stratified by risk level. <strong>The</strong> risk prediction model <strong>The</strong> statistical model used to forecast the risk <strong>of</strong> serious <strong>of</strong>fending is described in full in Berk et al. (2009). Random forests methods were applied to a dataset <strong>of</strong> all probation and parole cases in Philadelphia between 2002 and 2004, containing only the data available to probation <strong>of</strong>ficers at intake, 2 to predict the risk <strong>of</strong> being charged with a new serious crime within two years <strong>of</strong> the probation or parole case start date. Random forests is a statistical learning procedure that forecasts outcomes by aggregating results from multiple classification and regression trees. <strong>The</strong> model was designed to stratify the population according to APPD’s operational needs, with the assumption that the majority <strong>of</strong> the caseload was at low risk <strong>of</strong> serious recidivism and thus appropriate for lowintensity supervision. At the agency’s request, 61 per cent <strong>of</strong> cases were to be deemed low risk, with the remainder either high risk (approximately 10 per cent) or neither low nor high (approximately 30 per cent) (Fig. 3.1). APPD also deemed the proportions <strong>of</strong> false positives and false negatives expected in the final model to be operationally acceptable. <strong>The</strong> proportion <strong>of</strong> false positives (<strong>of</strong>fenders erroneously identified as lowrisk) was set at 5 per cent, and the proportion <strong>of</strong> false negatives (<strong>of</strong>fenders erroneously identified as high risk) was 20 per cent. A higher false negative rate was accepted given the lesser public safety concerns around this type <strong>of</strong> error. <strong>The</strong> initial 2002-2004 probation dataset is described as a “training sample,” which is used to ensure the 138
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University of Penn
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In memory of Edwar
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Janel McCaffrey and Knakiya Hagans,
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traditional practice, we find no ev
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Methodology .......................
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LIST OF FIGURES Figure 1.1: Effect
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intensity makes no difference to re
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isk of committing
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impact of probatio
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which perhaps suggests that ISP is
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program intended to reduce the stra
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supervision with probation
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3. To what extent do the risk/need
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Methods Scale (SMS: Farrington et a
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Types of Outcomes
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Description of met
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actual behavior. We analyze technic
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Treatment of quali
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treatment group, and those to the r
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Discussion of Resu
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were evaluated in the same report (
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direction of effec
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To what extent does program philoso
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Which other components of</
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.450; technical violations: Q B < .
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it should be noted that those progr
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up in unpublished reports than thos
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violations first went before a judg
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2 One U.S. estimate indicates that
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Tables Table 1.1: Characteristics <
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Table 1.3: Moderator Variable <stro
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Table 1.4: Moderator Variable <stro
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Figures Figure 1.1: Effect
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Figure 1.5: Effect of</stro
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CHAPTER 2. ‘Low-Intensity
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What Works in Probation Supervision
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to do with it. Furthermore, the mor
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to the New York City and Oregon stu
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Barnes et al. (forthcoming) have su
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paradox.” He suggests that ‘not
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fatal shootings in Philadelphia are
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the proportions of
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Interventions and Follow-Up Probati
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officers more <str
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1. Does the effect of</stro
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all, resulting in a large number <s
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Differential treatment take-up and
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control group members who received
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effect of LIS on r
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incarcerated must also be removed f
Page 101 and 102: offenders in our s
Page 103 and 104: ecidivists in the sample, 77 commit
Page 105 and 106: (IRR = 1.41, p ≤ .107). SES did n
Page 107 and 108: probability of fai
Page 109 and 110: The count model ou
Page 111 and 112: estimate is also practically identi
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Page 115 and 116: an end. On the other hand, research
Page 117 and 118: low-intensity supervision would imp
Page 119 and 120: Conclusion The aim
Page 121 and 122: We found that low-risk of</
Page 123 and 124: 7 LIS participants who were transfe
Page 125 and 126: Tables Table 2.1: Sample Characteri
Page 127 and 128: Table 2.3: Frequency of</st
Page 129 and 130: Table 2.5: Instrumental Variables M
Page 131 and 132: Table 2.7: Prevalence of</s
Page 133 and 134: Table 2.9: Time to Failure (Violent
Page 135 and 136: Table 2.12: Treatment Effect by Sub
Page 137 and 138: Table 2.14: Frequency of</s
Page 139 and 140: Table 2.16: Instrumental Variables
Page 141 and 142: Figures Figure 2.1: Risk-Based Allo
Page 143 and 144: Figure 2.5: Survival Time for LIS E
Page 145 and 146: CHAPTER 3. Risk Prediction for Effe
Page 147 and 148: sensitivity of the
Page 149 and 150: Programs should be designed to adhe
Page 151: weight information in an appropriat
Page 155 and 156: The Philadelphia m
Page 157 and 158: assignment pool. Backfill probation
Page 159 and 160: Little research has been done on th
Page 161 and 162: already predicted to be at low risk
Page 163 and 164: It assumes that if of</stro
Page 165 and 166: involved. Crimes that obviously met
Page 167 and 168: We examine the potential interactio
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Page 171 and 172: obberies. The only
Page 173 and 174: of low-intensity s
Page 175 and 176: classified as low risk. The
Page 177 and 178: All the risk ratios in Table 3.9 in
Page 179 and 180: 0.55. Raising the threshold results
Page 181 and 182: are partly selected on the basis <s
Page 183 and 184: make up the majority of</st
Page 185 and 186: strategy allows probation o
Page 187 and 188: Notes 1 In this study, murder, atte
Page 189 and 190: Table 3.2: Sample Characteristics (
Page 191 and 192: Table 3.6: Predictive Ability at Al
Page 193 and 194: Table 3.9: Post-Random Assignment O
Page 195 and 196: APPENDICES Appendix A: Systematic R
Page 197 and 198: combination. The s
Page 199 and 200: B. ELIGIBILITY CHECKLIST B1. First
Page 201 and 202: Other: txcxloth C6d. Additional pro
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D. SAMPLE LEVEL CODING SHEET Instru
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E. DEPENDENT VARIABLE CODING SHEET
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F14. Comparison group sample size f
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weight w is then recalculated (6),
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Study Sheffield (Males) 1976 (Folka
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Study Dallas TX 1992 (Turner & Pete
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Study Minnesota ISR 1995 (Deschenes
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Study Clark WA 2003 (Barnoski, 2003
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Study Whatcom WA 2003 (Barnoski, 20
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Study Los Angeles CA 2005 (Zhang &
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Fed. Prob., 56, 12-17. Turner, Pete
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Study Reference Benekos & Sonnenber
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Study Reference Giblin (2002) Using
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Study Reference Lasater et al. (n.d
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Study Reference Robertson (2000) Co
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Study Reference Turner & Greene (19
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Appendix E: Philadelphia APPD Low-<
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Prevalence of Reci
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Frequency of Recid
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Instrumental Variables Model: Preva
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Instrumental Variables Model: Preva
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Post-RA Months in Jail and Probabil
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Scaled Schoenfeld Residuals Time =
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BIBLIOGRAPHY Chapter 1 ** Denotes t
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Cook, T. D., & Campbell, D. T. (197
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Glaze, L. E, & Bonczar, T. P. (2009
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MacKenzie, D. L. (2006a). What work
Page 257 and 258:
Petersilia, J., Turner, S., & Desch
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safety first: 13 strategies for suc
Page 261 and 262:
Wilson, D. B. (2010). Meta-analysis
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Angrist, J. D. (2006). Instrumental
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Clear, T. R., & Hardyman, P. L. (19
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period266.pdf Lowenkamp, C. T., Lat
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Rosch, J. (2006). Deviant peer cont
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Taxman, F. S., & Thanner, M. (2006)
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statistical learning. Journal <stro
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Federal Bureau of
Page 277 and 278:
Marsh, K., & Fox, C., (2008). <stro
Page 279:
Sherman, L. W. (1993). Defiance, de
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