The recent inclusion of community-level risk variables in (some) fourth generation risk assessment instruments, ostensibly to make more accurate individual-level predictions of the likelihood of reoffending among the populations of probationers and parolees under community supervision, is examined in the following review. This development raises a thorny issue: what if the price of improved predictive accuracy is increased gender, race, or class-based disparity? Our review underscores the problems (conceptualization and measurement related) inherent in combining individual risk variables with community-level risk variables in order to assess an offender’s risk for re-offending during a specified follow-up period. In recognition of the likely disparity that will result from the conflation of neighborhood risk into individual risk assessments, we suggest an alternative: conduct a separate neighborhood risk assessment that can be used to simultaneously develop (1) a community-based treatment plan for individual offenders and (2) a resource development plan identifying and addressing service shortfalls and other risk factors in the neighborhoods where offenders reside.
AndrewsDABontaJ (2010) The Psychology of Criminal Conduct. Cambridge, MA: Elsevier Science.
2.
AndrewsDABontaJWormithJ (2006) The recent past and near future of risk and/or need assessment. Crime and Delinquency52(1): 7–27.
3.
AosSMillerMDrakeE (2006) Evidence-based Public Policy Options to Reduce Future Prison Construction, Criminal Justice Costs, and Crime Rates. Olympia, WA: Washington State Institute for Public Policy.
4.
AustinJ (2006) How much risk can we take? The misuse of risk assessment in corrections. Federal Probation70(2): 58–63.
5.
BairdC (2009) A Question of Evidence: A Critique of Risk Assessment Models Used in the Justice System. Madison, WI: National Council on Crime and Delinquency.
6.
BerkRBleichJ (2013) Statistical procedures for forecasting criminal behavior. Criminology & Public Policy12(3): 513–544.
7.
BerkRHyattJ (2015) Machine learning forecasts of risks in criminal justice settings. Federal Sentencing Reporter27(4): 222–228.
8.
BrennanT (2017) An alternative scientific paradigm for criminological risk assessment: Closed or open systems, or both? In: TaxmanF (ed.) Handbook on Risk and Need Assessment: Theory and Practice. New York: Routledge Taylor & Francis, 164–190.
9.
BriemanL (2001) Statistical modeling: The two cultures. Statistical Science16(3): 199–231.
10.
BurrellW (2017) Risk and needs assessment in probation and parole: The persistent gap between promise and practice. In: TaxmanF (ed.) Handbook on Risk and Need Assessment: Theory and Practice. New York: Routledge Taylor & Francis, 23–48.
11.
BurrellWGelbA (2007) You Get What You Measure: Compstat for Community Corrections. Washington, DC: Public Safety Performance Project, Pew Charitable Trusts.
12.
ByrneJ (2008) Editorial introduction: The social ecology of community corrections: Understanding the link between individual and community change. Criminology and Public Policy1(2): 1201–1213.
13.
ByrneJ (2009) Maximum Impact: Targeting Supervision on Higher-Risk People, Places and Times. Public Safety Performance Project, the PEW Center on the States, July.
14.
ByrneJPattavinaA (2006) Clinical and actuarial risk assessment in an evidence-based community corrections system: Issues to consider. Federal Probation70(3): 64–67.
15.
ByrneJSampsonR (1986) The Social Ecology of Crime. New York: Springer Verlag.
16.
ByrneJTaxmanF (2006) Crime control strategies and community change: Reframing the surveillance vs. treatment debate. Federal Probation70(1): 3–12.
17.
ClearT (2007) Imprisoning Communities: How Mass Incarceration Makes Disadvantaged Neighborhoods Worse. New York: Oxford University Press.
18.
DesmaraisSSinghJ (2013) Risk assessment instruments validated and implemented in correctional settings in the United States. New York: Council on State Governments Justice Center.
19.
GandyO (2007) Race and Cumulative Disadvantage: Engaging the Actuarial Assumption. The B. Aubrey Fisher Memorial Lecture, Department of Communication, University of Utah, 18 October.
20.
GottfredsonSMoriartyL (2006) Clinical versus actuarial judgements in criminal justice decisions: Should one replace the other?Federal Probation70(2): 15–18.
21.
GottfredsonSTaylorR (1986) Person-environment interactions in the prediction of recidivism. In: ByrneJSampsonR (eds) The Social Ecology of Crime. New York: Springer Verlag, 133–156.
22.
HessJTurnerS (2017) Accuracy of risk assessment in corrections population management: what’s the value added? In: TaxmanF (ed.) Handbook on Risk and Need Assessment: Theory and Practice. New York: Routledge, 93–113.
23.
HippJRPetersiliaJTurnerS (2010) Parolee recidivism in California: The effect of neighborhood context and social service agency characteristics. Criminology48(4): 947–979.
24.
JacobsonJ (2006) Do drug treatment facilities increase clients’ exposure to potential neighborhood-level triggers for relapse? A small-area assessment of a large, public treatment system. Journal of Urban Health83(2): 150–161.
25.
KubrinCStewertE (2006) Predicting who reoffends: The neglected role of neighborhood context in recidivism studies. Criminology44(1): 165–197.
26.
KubrinCSquiresGStewartE (2007) Neighborhoods, race, and recidivism: The community-re-offending nexus and its implications for African Americans. Race Relations Abstracts32(1): 7–37.
27.
LansingS (2012) New York State COMPAS-Probation Risk and Need Assessment Study: Examining the Recidivism Scale’s Effectiveness and Predictive Accuracy. New York: New York State Division of Criminal Justice Services, Office of Justice Research and Performance.
28.
LatessaELovinsB (2010) The role of offender risk assessment: A policy maker guide. Victims and Offenders5: 203–219.
McGarryPYaroniAAddieS (2014) Innovations in NYC Health and Human Services Policy Adult Probation and Neighborhood Opportunity Network Initiative. New York: VERA.
31.
MearsDBhatiA (2006) No community is an island: The effects of resource deprivation on urban violence in spatially and socially proximate communities. Criminology44(3): 509–547.
32.
NilssonAEstradaF (2007) Risky neighborhood or individuals at risk? The significance of neighborhood conditions for violent victimization in residential areas. Journal of Scandinavian Studies in Criminology and Crime Prevention8: 2–21.
33.
O’LearyVClearT (1984) Community Corrections in the1990’s. Washington, DC: National Institute of Corrections.
34.
PattavinaATaxmanF (2007) Community corrections and soft technology. In: ByrneJRebovichD (eds) The New Technology of Crime, Law, and Social Control. Monsey, NY: Criminal Justice Press, 327–346.
35.
PattavinaAMiofsky-TusinskiKByrneJ (2010) Innovation in community corrections: From monitoring technology to persuasive technology. Journal of Offender Monitoring23(1): 1–5.
36.
Pennsylvania Commission on Sentencing (2013) Risk/Needs Assessment Project, Interim Report 7, Validation of Risk Scale. Available at: http://pasentencing.us(accessed 15 January 2017).
37.
ReisigMHoltfreterKMorashM (2006) Assessing recidivism risk across female pathways to crime. Justice Quarterly23: 384–405.
38.
RoseDina RToddR Clear (2003) Incarceration, reentry, and social capital: Social networks in the balance. In: JeremyTravisMichelleWaul (eds.), Prisoners Once Removed. Washington, DC: Urban Institute Press.
39.
SampsonR (1993) The community context of violent crime. In: WilsonWJ (ed.) Sociology and the Public Agenda. Newbury Park, CA: Sage, 259–286.
40.
SampsonR (2008) Moving to inequality: Neighborhood effects and experiments meet social structure. American Journal of Sociology114(1): 189–231.
41.
SampsonRJMorenoffJDGannon-RowleyT (2002) Assessing ‘neighborhood effects’: Social processes and new directions in research. Annual Review of Sociology28: 443–478.
42.
SmithPCullenFTLatessaEJ (2009) Can 14,737 women be wrong? A meta-analysis of the LSI-R and recidivism for female offenders. Criminology & Public Policy8(1): 183–208.
43.
StahlerGMennisJBelenkoSWelshWHillerMZajacG (2013) Predicting recidivism for released state prison offenders: Examining the influence of individual and neighborhood characteristics and spatial contagion on the likelihood of reincarceration. Criminal Justice & Behavior40(6): 690–711.
44.
TaxmanF (ed.) (2017) Handbook on Risk and Need Assessment: Theory and Practice. New York: Routledge Taylor & Francis
45.
TaxmanFPattavinaA (2013) Simulation Strategies to Reduce Recidivism: Risk Need Responsivity (RNR) Modeling for the Criminal Justice System. New York: Springer.
46.
TaxmanFPattavinaACaudyMByrneJDursoJ (2013) The empirical basis for the RNR model with an updated conceptual framework. In: TaxmanFPattavinaA (eds) Simulation Strategies to Reduce Recidivism: Risk Need Responsivity (RNR) Modeling for the Criminal Justice System. New York: Springer, 73–114.
47.
TollenaarNVan der HeijdenP (2013) Which method predicts recidivism best?: A comparison of statistical, machine learning and data mining predictive models. Journal of the Royal Statistical Society Series A176(2): 565–584.
