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1
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27744556941
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Adolescents at Risk for Violence: An Initial Validation of the Life Challenges Questionnaire and Risk Assessment Index, 40
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Ilyse Grinberg et al., Adolescents at Risk for Violence: An Initial Validation of the Life Challenges Questionnaire and Risk Assessment Index, 40 ADOLESCENCE 573, 573-74 (2005).
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(2005)
ADOLESCENCE
, vol.573
, pp. 573-574
-
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Grinberg, I.1
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2
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33947696641
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See OFFICE OF JUVENILE JUSTICE & DELINQUENCY PREVENTION, COMPREHENSIVE STRATEGY FOR SERIOUS, VIOLENT, AND CHRONIC JUVENILE OFFENDERS 7 (1998) (discussing states' responses in attempting to fulfill responsibilities of juvenile justice system).
-
See OFFICE OF JUVENILE JUSTICE & DELINQUENCY PREVENTION, COMPREHENSIVE STRATEGY FOR SERIOUS, VIOLENT, AND CHRONIC JUVENILE OFFENDERS 7 (1998) (discussing states' responses in attempting to fulfill responsibilities of juvenile justice system).
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3
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33947663926
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See TED PALMER, THE RE-EMERGENCE OF CORRECTIONAL INTERVENTION 35-39 (1992) (discussing studies that show effectiveness of treatments where specific programs are chosen depending on certain characteristics of an offender).
-
See TED PALMER, THE RE-EMERGENCE OF CORRECTIONAL INTERVENTION 35-39 (1992) (discussing studies that show effectiveness of treatments where specific programs are chosen depending on certain characteristics of an offender).
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4
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33947700323
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See KELLY HANNAH-MOFFAT & PAULA MAURUTTO, DEP'T OF JUSTICE CAN., YOUTH RISK/NEED ASSESSMENT: AN OVERVIEW OF ISSUES AND PRACTICES § 1.0, at 1 (2003), http://www.justice.gc.ca/en/ps/rs/rep/2003/rr03yj-4/rr03yj- 4_1.html (noting that classifying risks and needs of youths using new actuarial and quasi-actuarial techniques is growing trend among researchers).
-
See KELLY HANNAH-MOFFAT & PAULA MAURUTTO, DEP'T OF JUSTICE CAN., YOUTH RISK/NEED ASSESSMENT: AN OVERVIEW OF ISSUES AND PRACTICES § 1.0, at 1 (2003), http://www.justice.gc.ca/en/ps/rs/rep/2003/rr03yj-4/rr03yj- 4_1.html (noting that classifying risks and needs of youths using "new actuarial and quasi-actuarial techniques" is growing trend among researchers).
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-
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5
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33947637111
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For a review of literature dealing with methods of risk assessment currently used in juvenile justice and child welfare, see generally Tim Brennan, Classification: An Overview of Selected Methodological Issues, in PREDICTION & CLASSIFICATION: CRIMINAL JUSTICE DECISION MAKING 201 (Don M. Gottfredson & Michael Tonry eds, 1987, calling for upgrade in quality of classification methods by integrating interdisciplinary methodological frameworks);
-
For a review of literature dealing with methods of risk assessment currently used in juvenile justice and child welfare, see generally Tim Brennan, Classification: An Overview of Selected Methodological Issues, in PREDICTION & CLASSIFICATION: CRIMINAL JUSTICE DECISION MAKING 201 (Don M. Gottfredson & Michael Tonry eds., 1987) (calling for upgrade in quality of classification methods by integrating interdisciplinary methodological frameworks);
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-
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6
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33947631328
-
-
Stephen D. Gottfredson, Prediction: An Overview of Selected Methodological Issues, in PREDICTION & CLASSIFICATION: CRIMINAL JUSTICE DECISION MAKING, supra, at 21 (discussing why actuarial approach methods are superior and describing methods of analysis);
-
Stephen D. Gottfredson, Prediction: An Overview of Selected Methodological Issues, in PREDICTION & CLASSIFICATION: CRIMINAL JUSTICE DECISION MAKING, supra, at 21 (discussing why actuarial approach methods are superior and describing methods of analysis);
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-
-
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7
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33947625006
-
-
Roger Tarling & John A. Perry, Statistical Methods in Criminological Prediction, in PREDICTION IN CRIMINOLOGY 210 (David P. Farrington & Roger Tarling eds., 1985) (comparing various statistical predictive techniques);
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Roger Tarling & John A. Perry, Statistical Methods in Criminological Prediction, in PREDICTION IN CRIMINOLOGY 210 (David P. Farrington & Roger Tarling eds., 1985) (comparing various statistical predictive techniques);
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-
-
-
8
-
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33947631329
-
-
Leslie T. Wilkins, The Politics of Prediction, in PREDICTION IN CRIMINOLOGY, supra, at 34 (discussing superiority of statistical over clinical analyses and moral implications of predictive assessments);
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Leslie T. Wilkins, The Politics of Prediction, in PREDICTION IN CRIMINOLOGY, supra, at 34 (discussing superiority of statistical over clinical analyses and moral implications of predictive assessments);
-
-
-
-
9
-
-
0033218127
-
-
Christopher Baird et al., Risk Assessment in Child Protective Services: Consensus and Actuarial Model Reliability, 78 CHILD WELFARE 723 (1999) (comparing actuarial-based systems with consensus-based systems);
-
Christopher Baird et al., Risk Assessment in Child Protective Services: Consensus and Actuarial Model Reliability, 78 CHILD WELFARE 723 (1999) (comparing actuarial-based systems with consensus-based systems);
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-
-
-
10
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0024981793
-
-
Robyn M. Dawes et al., Clinical Versus Actuarial Judgment, 243 SCI. 1668 (1989) (finding actuarial methods superior at predicting human behavior over clinical methods);
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Robyn M. Dawes et al., Clinical Versus Actuarial Judgment, 243 SCI. 1668 (1989) (finding actuarial methods superior at predicting human behavior over clinical methods);
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-
-
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11
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0036354679
-
-
Robert D. Hoge, Standardized Instruments for Assessing Risk and Need in Youthful Offenders, 29 CRIM. JUST. & BEHAV. 380 (2002) (comparing three standardized risk assessment measures and explaining why they are preferred over clinical methods).
-
Robert D. Hoge, Standardized Instruments for Assessing Risk and Need in Youthful Offenders, 29 CRIM. JUST. & BEHAV. 380 (2002) (comparing three standardized risk assessment measures and explaining why they are preferred over clinical methods).
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-
-
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12
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33947685893
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-
See also Peter R. Jones, Risk Prediction in Criminal Justice, in CHOOSING CORRECTIONAL OPTIONS THAT WORK 33, 35 (Alan T. Harland ed., 1995) (stating that evidence points to success of statistical over clinical prediction methods);
-
See also Peter R. Jones, Risk Prediction in Criminal Justice, in CHOOSING CORRECTIONAL OPTIONS THAT WORK 33, 35 (Alan T. Harland ed., 1995) (stating that evidence points to success of statistical over clinical prediction methods);
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-
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13
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84984364515
-
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D.A. Andrews et al., Does Correctional Treatment Work? A Clinically Relevant and Psychologically Informed Meta-Analysis, 28 CRIMINOLOGY 369, 377-80 (1990) (providing examples of statistical analyses);
-
D.A. Andrews et al., Does Correctional Treatment Work? A Clinically Relevant and Psychologically Informed Meta-Analysis, 28 CRIMINOLOGY 369, 377-80 (1990) (providing examples of statistical analyses);
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-
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14
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0034311219
-
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Christopher Baird & Dennis Wagner, The Relative Validity of Actuarial- and Consensus-Based Risk Assessment Systems, 22 CHILD. & YOUTH SERVS. REV. 839, 867 (2000) (finding actuarial-based systems more accurate than consensus-based systems).
-
Christopher Baird & Dennis Wagner, The Relative Validity of Actuarial- and Consensus-Based Risk Assessment Systems, 22 CHILD. & YOUTH SERVS. REV. 839, 867 (2000) (finding actuarial-based systems more accurate than consensus-based systems).
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-
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15
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33947643577
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-
According to the NCCD website, the organization has collaborated with over seventy-five state and local jurisdictions as well as with the United States Department of Justice. More information about the NCCD can be found at The National Council on Crime and Delinquency, http://www.nccd-crc.org/nccd/ n_index_main.html (last visited Jan. 2, 2007).
-
According to the NCCD website, the organization has collaborated with over seventy-five state and local jurisdictions as well as with the United States Department of Justice. More information about the NCCD can be found at The National Council on Crime and Delinquency, http://www.nccd-crc.org/nccd/ n_index_main.html (last visited Jan. 2, 2007).
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-
-
-
16
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5744229105
-
-
See Baird & Wagner, supra note 5, at 842-43 (discussing accuracy of actuarial risk assessment systems); Dawes et al., supra note 5, at 1673 (determining that actuarial method of predicting human behavior is superior to clinical method because its accuracy can be more easily monitored); Jones, supra note 5, at 35 (stating that evidence points to success of statistical over clinical prediction methods); David R. Schwartz et al., Computational Intelligence Techniques for Risk Assessment and Decision Support, 26 CHILD. & YOUTH SERVS. REV. 1081, 1082-83 (2004) (suggesting that actuarial risk assessment methods are more reliable than consensus or clinical methods).
-
See Baird & Wagner, supra note 5, at 842-43 (discussing accuracy of actuarial risk assessment systems); Dawes et al., supra note 5, at 1673 (determining that actuarial method of predicting human behavior is superior to clinical method because its accuracy can be more easily monitored); Jones, supra note 5, at 35 (stating that evidence points to success of statistical over clinical prediction methods); David R. Schwartz et al., Computational Intelligence Techniques for Risk Assessment and Decision Support, 26 CHILD. & YOUTH SERVS. REV. 1081, 1082-83 (2004) (suggesting that actuarial risk assessment methods are more reliable than consensus or clinical methods).
-
-
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17
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33947652634
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quality controls
-
See, note 5, at, explaining that although actuarial methods have great benefits, should be used to ensure that actuarial method is appropriately working
-
See Dawes et al., supra note 5, at 1673 (explaining that although actuarial methods have great benefits, "quality controls" should be used to ensure that actuarial method is appropriately working).
-
supra
, pp. 1673
-
-
Dawes1
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18
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33947645311
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-
For a discussion of studies that have demonstrated the greater effectiveness of empirical approaches over clinical approaches, see generally John Monahan, The Scientific Status of Research on Clinical and Actuarial Predictions of Violence, in 2 SCIENCE IN THE LAW: SOCIAL AND BEHAVIORAL SCIENCE ISSUES § 2-2.1.1(2);
-
For a discussion of studies that have demonstrated the greater effectiveness of empirical approaches over clinical approaches, see generally John Monahan, The Scientific Status of Research on Clinical and Actuarial Predictions of Violence, in 2 SCIENCE IN THE LAW: SOCIAL AND BEHAVIORAL SCIENCE ISSUES § 2-2.1.1(2);
-
-
-
-
19
-
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33947614053
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-
(David L. Faigman et al. eds., 2002); Tarling & Perry, supra note 5; Dawes et al., supra note 5; William M. Grove & Paul E. Meehl, Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedure: The Clinical-Statistical Controversy, 2 PSYCHOL. PUB. POL'Y & L. 293 (1996).
-
(David L. Faigman et al. eds., 2002); Tarling & Perry, supra note 5; Dawes et al., supra note 5; William M. Grove & Paul E. Meehl, Comparative Efficiency of Informal (Subjective, Impressionistic) and Formal (Mechanical, Algorithmic) Prediction Procedure: The Clinical-Statistical Controversy, 2 PSYCHOL. PUB. POL'Y & L. 293 (1996).
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-
-
-
20
-
-
33947708316
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-
For more recent reviews of evaluations of actuarial assessment tools, see generally Grinberg et al., supra note 1; Shane R. Jimerson et al., The Santa Barbara Assets and Risks Assessment to Predict Recidivism Among Male and Female Juveniles: An Investigation of Inter-rater Reliability and Predictive Validity, 27 EDUC. & TREATMENT CHILD. 353 (2004);
-
For more recent reviews of evaluations of actuarial assessment tools, see generally Grinberg et al., supra note 1; Shane R. Jimerson et al., The Santa Barbara Assets and Risks Assessment to Predict Recidivism Among Male and Female Juveniles: An Investigation of Inter-rater Reliability and Predictive Validity, 27 EDUC. & TREATMENT CHILD. 353 (2004);
-
-
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21
-
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23944470412
-
-
Cornells Stadtland et al., Risk Assessment and Prediction of Violent and Sexual Recidivism in Sex Offenders: Long-term Predictive Validity of Four Risk Assessment Instruments, 16 J. FORENSIC PSYCHIATRY & PSYCHOL. 92 (2005).
-
Cornells Stadtland et al., Risk Assessment and Prediction of Violent and Sexual Recidivism in Sex Offenders: Long-term Predictive Validity of Four Risk Assessment Instruments, 16 J. FORENSIC PSYCHIATRY & PSYCHOL. 92 (2005).
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-
-
-
22
-
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33947700874
-
-
See infra Parts III.B-D for a comparison of the performance of the Wisconsin Delinquency Risk Assessment, the customized model using configural analysis, and the customized model using neural networks.
-
See infra Parts III.B-D for a comparison of the performance of the Wisconsin Delinquency Risk Assessment, the customized model using configural analysis, and the customized model using neural networks.
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-
-
-
23
-
-
33947673842
-
-
See infra Part III.E for an examination of results of the comparison in relation to juvenile risk placement.
-
See infra Part III.E for an examination of results of the comparison in relation to juvenile risk placement.
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-
-
-
24
-
-
33947657839
-
-
See infra Part III.F for an examination of the accuracy of each method in predicting reoffenders.
-
See infra Part III.F for an examination of the accuracy of each method in predicting reoffenders.
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-
-
-
25
-
-
33947639941
-
-
See infra Part III.A for an overview of the demographic data used in these assessments.
-
See infra Part III.A for an overview of the demographic data used in these assessments.
-
-
-
-
26
-
-
33947688077
-
-
Seventy-three agencies representing forty-four states and twenty-four localities, collectively supervising well over half that nation's probationers and parolees, reported that just over one-third of agencies used the Wisconsin instrument or an instrument based on the Wisconsin model. U.S. DEP'T OF JUSTICE, NAT'L INST. OF CORR., TOPICS IN COMMUNITY CORRECTIONS: OFFENDER ASSESSMENT 4 (2003).
-
Seventy-three agencies representing forty-four states and twenty-four localities, collectively supervising well over half that nation's probationers and parolees, reported that just over one-third of agencies used the Wisconsin instrument or an instrument based on the Wisconsin model. U.S. DEP'T OF JUSTICE, NAT'L INST. OF CORR., TOPICS IN COMMUNITY CORRECTIONS: OFFENDER ASSESSMENT 4 (2003).
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-
-
-
27
-
-
33947638385
-
-
See infra Part III.C for an analysis of the customized model using configural analysis.
-
See infra Part III.C for an analysis of the customized model using configural analysis.
-
-
-
-
28
-
-
33947683249
-
-
See infra Part III.D for an analysis of the customized model using neural networks.
-
See infra Part III.D for an analysis of the customized model using neural networks.
-
-
-
-
29
-
-
33947650003
-
-
See Baird & Wagner, supra note 5, at 840 (stating that, as of 1996, at least thirty-eight out of fifty-four U.S. states and territories used formal risk assessment tools; twenty-six implemented tools after 1987).
-
See Baird & Wagner, supra note 5, at 840 (stating that, as of 1996, at least thirty-eight out of fifty-four U.S. states and territories used formal risk assessment tools; twenty-six implemented tools after 1987).
-
-
-
-
30
-
-
33947669332
-
-
Id. at 842; Dawes et al., supra note 5, at 1671-72; Grove & Meehl, supra note 9, at 293-95.
-
Id. at 842; Dawes et al., supra note 5, at 1671-72; Grove & Meehl, supra note 9, at 293-95.
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-
-
-
31
-
-
33947654801
-
-
See PAUL E. MEEHL, CLINICAL VERSUS STATISTICAL PREDICTION: A THEORETICAL ANALYSIS AND A REVIEW OF THE EVIDENCE 3-4, 136 (1954, labeling clinical methods as hypothesizing predictors of risk as opposed to actuarial methods that use statistics and concluding that use of actuarial method is unavoidable because clinical methods are untrustworthy, Baird et al, supra note 5, at 743 (finding that actuarial methods are more consistent and reliable than consensus, or clinical, methods, Dawes et al, supra note 5, at 1671 finding that actuarial methods are more accurate than clinical judgment methods because clinical methods rely on independent judgments that can dramatically fluctuate and are not statistically significant
-
See PAUL E. MEEHL, CLINICAL VERSUS STATISTICAL PREDICTION: A THEORETICAL ANALYSIS AND A REVIEW OF THE EVIDENCE 3-4, 136 (1954) (labeling clinical methods as hypothesizing predictors of risk as opposed to actuarial methods that use statistics and concluding that use of actuarial method is "unavoidable" because clinical methods are untrustworthy); Baird et al., supra note 5, at 743 (finding that actuarial methods are more consistent and reliable than consensus, or clinical, methods); Dawes et al., supra note 5, at 1671 (finding that actuarial methods are more accurate than clinical judgment methods because clinical methods rely on independent judgments that can dramatically fluctuate and are not statistically significant).
-
-
-
-
32
-
-
33947672814
-
-
Baird & Wagner, supra note 5, at 839-40
-
Baird & Wagner, supra note 5, at 839-40.
-
-
-
-
33
-
-
33947642527
-
-
See Jones, supra note 5, at 43 (discussing programs developed through assistance from the NIC); Baird & Wagner, supra note 5, at 842 (stating that NCCD has conducted actuarial research in at least seven states since 1989). See generally OFFICE OF JUVENILE JUSTICE & DELINQUENCY PREVENTION, supra note 2 (introducing its strategy for dealing with varying degrees of juvenile offenders).
-
See Jones, supra note 5, at 43 (discussing programs developed through assistance from the NIC); Baird & Wagner, supra note 5, at 842 (stating that NCCD has conducted actuarial research in at least seven states since 1989). See generally OFFICE OF JUVENILE JUSTICE & DELINQUENCY PREVENTION, supra note 2 (introducing its strategy for dealing with varying degrees of juvenile offenders).
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-
-
-
34
-
-
33947640493
-
-
See infra Part III.B for a list of the eight factors.
-
See infra Part III.B for a list of the eight factors.
-
-
-
-
35
-
-
0347154899
-
-
See generally Baird & Wagner, supra note 5 (comparing Washington Risk Assessment Matrix with California Family Assessment Factor Analysis and Michigan Structured Decision Making System's Family Risk Assessment of Abuse and Neglect); Peter R. Jones et al., Identifying Chronic Juvenile Offenders, 18 JUST. Q. 479 (2001) (describing study identifying chronic juvenile offenders in Orange County, California).
-
See generally Baird & Wagner, supra note 5 (comparing Washington Risk Assessment Matrix with California Family Assessment Factor Analysis and Michigan Structured Decision Making System's Family Risk Assessment of Abuse and Neglect); Peter R. Jones et al., Identifying Chronic Juvenile Offenders, 18 JUST. Q. 479 (2001) (describing study identifying chronic juvenile offenders in Orange County, California).
-
-
-
-
36
-
-
33947624104
-
-
See Hoge, supra note 5, at 390-91 (explaining YLS/CMI method). See generally HANNAH-MOFFAT & MAURUTTO, supra note 4, § 4.0, at 9-13 (reviewing and critiquing risk assessment procedures in Canada, including YLS/CMI); David P. Cole & Glenn Angus, Using Pre-Sentence Reports to Evaluate and Respond to Risk, 47 CRIM. L.Q. 302 (2003) (describing benefits of presentence reports in risk assessment);
-
See Hoge, supra note 5, at 390-91 (explaining YLS/CMI method). See generally HANNAH-MOFFAT & MAURUTTO, supra note 4, § 4.0, at 9-13 (reviewing and critiquing risk assessment procedures in Canada, including YLS/CMI); David P. Cole & Glenn Angus, Using Pre-Sentence Reports to Evaluate and Respond to Risk, 47 CRIM. L.Q. 302 (2003) (describing benefits of presentence reports in risk assessment);
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-
-
-
37
-
-
10244235371
-
-
Ivan Zinger, Actuarial Risk Assessment and Human Rights: A Commentary, 46 CANADIAN J. CRIMINOLOGY & CRIM. JUST. 607, 609 (2004) (commenting on accuracy of third-generation actuarial risk assessment techniques, particularly Canada's LSI-R, in predicting recidivism).
-
Ivan Zinger, Actuarial Risk Assessment and Human Rights: A Commentary, 46 CANADIAN J. CRIMINOLOGY & CRIM. JUST. 607, 609 (2004) (commenting on accuracy of third-generation actuarial risk assessment techniques, particularly Canada's LSI-R, in predicting recidivism).
-
-
-
-
38
-
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33947679218
-
-
OFFICE OF JUVENILE JUSTICE & DELINQUENCY PREVENTION, supra note 2, at 13-14
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OFFICE OF JUVENILE JUSTICE & DELINQUENCY PREVENTION, supra note 2, at 13-14.
-
-
-
-
39
-
-
30344448597
-
-
For examples of each type of study, see generally D.A. Andrews et al, The Recent Past and Near Future of Risk and/or Need Assessment, 52 CRIME & DELINQ. 7 (2006, comparing performance of different types of empirical methods, Baird & Wagner, supra note 5 (comparing predictive validity of actuarial- and consensus-based or clinical methods, Grinberg et al, supra note 1 validating existing actuarial method
-
For examples of each type of study, see generally D.A. Andrews et al., The Recent Past and Near Future of Risk and/or Need Assessment, 52 CRIME & DELINQ. 7 (2006) (comparing performance of different types of empirical methods); Baird & Wagner, supra note 5 (comparing predictive validity of actuarial- and consensus-based or clinical methods); Grinberg et al., supra note 1 (validating existing actuarial method).
-
-
-
-
40
-
-
33947651579
-
-
See Stephen D. Gottfredson & Don M. Gottfredson, Accuracy of Prediction Models, in 2 CRIMINAL CAREERS AND CAREER CRIMINALS 212, 247 (Alfred Blumstein et al. eds., 1986) (explaining that various studies have determined statistical predictive devices are more accurate than clinical methods); Jones, supra note 5, at 35-36 (suggesting that all available evidence regards statistical predictive devices over clinical devices); Baird & Wagner, supra note 5, at 842-43 (noting that research has shown superiority of actuarial method over clinical method); Dawes et al., supra note 5, at 1673 (reviewing research that propels theory that actuarial assessment is as accurate or more accurate at prediction than clinical assessment).
-
See Stephen D. Gottfredson & Don M. Gottfredson, Accuracy of Prediction Models, in 2 CRIMINAL CAREERS AND "CAREER CRIMINALS" 212, 247 (Alfred Blumstein et al. eds., 1986) (explaining that various studies have determined statistical predictive devices are more accurate than clinical methods); Jones, supra note 5, at 35-36 (suggesting that all available evidence regards statistical predictive devices over clinical devices); Baird & Wagner, supra note 5, at 842-43 (noting that research has shown superiority of actuarial method over clinical method); Dawes et al., supra note 5, at 1673 (reviewing research that propels theory that actuarial assessment is as accurate or more accurate at prediction than clinical assessment).
-
-
-
-
41
-
-
33947660847
-
-
Gottfredson & Gottfredson, supra note 28, at 247
-
Gottfredson & Gottfredson, supra note 28, at 247.
-
-
-
-
42
-
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33947613553
-
-
See Monahan, supra note 9, § 2-2.1.1(2), at 102-03 (discussing actuarial study indicating that substance abuse, prior arrests for violent crimes, and young age were significant predictors of future violence); Tarling & Perry, supra note 5, at 212-13 (explaining that variables used to predict reconviction can remain relatively constant); Dawes et al., supra note 5, at 1671 (stating that actuarial tools are effective in identifying significant variables that contributed to a decision); Grove & Meehl, supra note 9, at 317 (stating that actuarial data allows most influential variables to be identified).
-
See Monahan, supra note 9, § 2-2.1.1(2), at 102-03 (discussing actuarial study indicating that substance abuse, prior arrests for violent crimes, and young age were significant predictors of future violence); Tarling & Perry, supra note 5, at 212-13 (explaining that variables used to predict reconviction can remain relatively constant); Dawes et al., supra note 5, at 1671 (stating that actuarial tools are effective in identifying significant variables that contributed to a decision); Grove & Meehl, supra note 9, at 317 (stating that actuarial data allows most influential variables to be identified).
-
-
-
-
43
-
-
33947611991
-
-
See Gottfredson & Gottfredson, supra note 28, at 247-48 (citing various studies in which results from actuarial devices were superior to individual human's judgment).
-
See Gottfredson & Gottfredson, supra note 28, at 247-48 (citing various studies in which results from actuarial devices were superior to individual human's judgment).
-
-
-
-
44
-
-
0034311446
-
-
Eileen Gambrill & Aron Shlonsky, Risk Assessment in Context, 22 CHILD. & YOUTH SERVS. REV. 813, 825 (2000).
-
Eileen Gambrill & Aron Shlonsky, Risk Assessment in Context, 22 CHILD. & YOUTH SERVS. REV. 813, 825 (2000).
-
-
-
-
45
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33947688617
-
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Id. at 826
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Id. at 826.
-
-
-
-
46
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33947673853
-
-
For a review of such literature, see JESS MCDONALD & JOHN GOAD, ILL. DEP'T OF CHILDREN & FAMILY SERVS, ILLINOIS CHILD ENDANGERMENT RISK ASSESSMENT PROTOCOL 10 (2002, available at http://www.state.il.us/dcfs/docs/cerap2002.pdf (concluding that Child Endangerment Risk Assessment Protocol reduced recurrence rates of maltreatment for at-risk children, Grinberg et al, supra note 1, at 598 (finding initial support for validity of The Life-Challenges Questionnaire and Risk Assessment Index as a risk assessment measure, Jimerson et al, supra note 10, at 370 (concluding that Santa Barbara Assets and Risks Assessment provides enhanced assessment of juveniles for both males and females, Stadtland et al, supra note 10, at 105-06 finding that various assessment tools efficiently predicted violent nons
-
For a review of such literature, see JESS MCDONALD & JOHN GOAD, ILL. DEP'T OF CHILDREN & FAMILY SERVS., ILLINOIS CHILD ENDANGERMENT RISK ASSESSMENT PROTOCOL 10 (2002), available at http://www.state.il.us/dcfs/docs/cerap2002.pdf (concluding that Child Endangerment Risk Assessment Protocol reduced recurrence rates of maltreatment for at-risk children); Grinberg et al., supra note 1, at 598 (finding "initial support" for validity of The Life-Challenges Questionnaire and Risk Assessment Index as a risk assessment measure); Jimerson et al., supra note 10, at 370 (concluding that Santa Barbara Assets and Risks Assessment provides enhanced assessment of juveniles for both males and females); Stadtland et al., supra note 10, at 105-06 (finding that various assessment tools efficiently predicted violent nonsexual, noncontact sexual, and contact sexual recidivism).
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-
-
-
47
-
-
33947651587
-
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See, e.g., HANNAH-MOFFAT & MAURUTTO, supra note 4, § 4.0, at 9-10, § 6.1, at 27 (noting that government and contract researchers conducted many of these studies and suggesting that independent researchers should be conducting these studies to ensure lack of bias).
-
See, e.g., HANNAH-MOFFAT & MAURUTTO, supra note 4, § 4.0, at 9-10, § 6.1, at 27 (noting that government and contract researchers conducted many of these studies and suggesting that independent researchers should be conducting these studies to ensure lack of bias).
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-
-
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48
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33947641519
-
-
Tarling & Perry, supra note 5, at 211 (On the basis of her results and those of other prediction studies that she reviewed, Simon argued that no method was greatly superior to any other and concluded that 'from this examination of statistical methods for combining data . . . in practice all of them work about equally well.' (quoting FRANCES H. SIMON, HOME OFFICE RESEARCH STUDY No. 7, PREDICTION METHODS IN CRIMINOLOGY: INCLUDING A PREDICTION STUDY OF YOUNG MEN ON PROBATION 154 (1971)).
-
Tarling & Perry, supra note 5, at 211 ("On the basis of her results and those of other prediction studies that she reviewed, Simon argued that no method was greatly superior to any other and concluded that 'from this examination of statistical methods for combining data . . . in practice all of them work about equally well.'" (quoting FRANCES H. SIMON, HOME OFFICE RESEARCH STUDY No. 7, PREDICTION METHODS IN CRIMINOLOGY: INCLUDING A PREDICTION STUDY OF YOUNG MEN ON PROBATION 154 (1971)).
-
-
-
-
49
-
-
33947691579
-
-
Jones, supra note 5, at 43-44
-
Jones, supra note 5, at 43-44.
-
-
-
-
50
-
-
33947676787
-
-
Gottfredson, supra note 5, at 24-25
-
Gottfredson, supra note 5, at 24-25.
-
-
-
-
51
-
-
33947695620
-
-
PETER SCHMIDT & ANN DRYDEN WITTE, PREDICTING RECIDIVISM USING SURVIVAL MODELS 13-14 (1988).
-
PETER SCHMIDT & ANN DRYDEN WITTE, PREDICTING RECIDIVISM USING SURVIVAL MODELS 13-14 (1988).
-
-
-
-
52
-
-
33947656381
-
-
David P. Farrington & Roger Tarling, Criminological Prediction: The Way Forward, in PREDICTION IN CRIMINOLOGY, supra note 5, at 264.
-
David P. Farrington & Roger Tarling, Criminological Prediction: The Way Forward, in PREDICTION IN CRIMINOLOGY, supra note 5, at 264.
-
-
-
-
53
-
-
33947615102
-
-
See Gottfredson, supra note 5, at 26-27 explaining how statistical methods sometimes assume that results from one sample can explain results from similar sample
-
See Gottfredson, supra note 5, at 26-27 (explaining how statistical methods sometimes assume that results from one sample can explain results from similar sample).
-
-
-
-
54
-
-
33947621500
-
-
For examples of studies of human behavior from nontraditional disciplines, see generally ALBERT NIGRIN, NEURAL NETWORKS FOR PATTERN RECOGNITION (1993, studying frameworks of pattern recognition by neural networks);
-
For examples of studies of human behavior from nontraditional disciplines, see generally ALBERT NIGRIN, NEURAL NETWORKS FOR PATTERN RECOGNITION (1993) (studying frameworks of pattern recognition by neural networks);
-
-
-
-
55
-
-
0033710931
-
-
T.A. Arentze et al., Using Decision Tree Induction Systems for Modeling Space-Time Behavior, 32 GEOGRAPHICAL ANALYSIS 330 (2000) (discussing use of decision tree induction systems to predict an individual's activity and travel choices).
-
T.A. Arentze et al., Using Decision Tree Induction Systems for Modeling Space-Time Behavior, 32 GEOGRAPHICAL ANALYSIS 330 (2000) (discussing use of decision tree induction systems to predict an individual's activity and travel choices).
-
-
-
-
56
-
-
33947687652
-
-
Tarling & Perry, supra note 5, at 227
-
Tarling & Perry, supra note 5, at 227.
-
-
-
-
58
-
-
33947677586
-
-
Id
-
Id.
-
-
-
-
59
-
-
33947671309
-
-
RODERICK J.A. LITTLE & DONALD B. RUBIN, STATISTICAL ANALYSIS WITH MISSING DATA 60-61 (1987).
-
RODERICK J.A. LITTLE & DONALD B. RUBIN, STATISTICAL ANALYSIS WITH MISSING DATA 60-61 (1987).
-
-
-
-
60
-
-
33947636583
-
-
See generally DEAN J. CHAMPION, MEASURING OFFENDER RISK: A CRIMINAL JUSTICE SOURCEBOOK (1994) (reviewing thoroughly a range of issues pertaining to development and application of risk assessment within criminal and juvenile justice without once referring to problems of missing data).
-
See generally DEAN J. CHAMPION, MEASURING OFFENDER RISK: A CRIMINAL JUSTICE SOURCEBOOK (1994) (reviewing thoroughly a range of issues pertaining to development and application of risk assessment within criminal and juvenile justice without once referring to problems of missing data).
-
-
-
-
61
-
-
33947657855
-
-
But see DON M. GOTTFREDSON & HOWARD M. SNYDER, OFFICE OF JUVENILE JUSTICE & DELINQUENCY PREVENTION, NATIONAL CENTER FOR JUVENILE JUSTICE REPORT, THE MATHEMATICS OF RISK CLASSIFICATION: CHANGING DATA INTO VALID INSTRUMENTS FOR JUVENILE COURTS 6 (2005), available at http://www.ncjrs.gov/html/ojjdp/ 209158 (dealing specifically with the mathematical aspects of risk assessment and stating that missing data cause great problems).
-
But see DON M. GOTTFREDSON & HOWARD M. SNYDER, OFFICE OF JUVENILE JUSTICE & DELINQUENCY PREVENTION, NATIONAL CENTER FOR JUVENILE JUSTICE REPORT, THE MATHEMATICS OF RISK CLASSIFICATION: CHANGING DATA INTO VALID INSTRUMENTS FOR JUVENILE COURTS 6 (2005), available at http://www.ncjrs.gov/html/ojjdp/ 209158 (dealing specifically with the mathematical aspects of risk assessment and stating that "missing data cause great problems").
-
-
-
-
62
-
-
33947669331
-
-
See Jones, note 5, at, detailing various studies attempting to predict criminality since the
-
See Jones, supra note 5, at 36 (detailing various studies attempting to predict criminality since the 1920s).
-
(1920)
supra
, pp. 36
-
-
-
63
-
-
33947679217
-
-
See generally Ernest W. Burgess, Factors Determining Success or Failure on Parole, in THE WORKINGS OF THE INDETERMINATE-SENTENCE LAW AND THE PAROLE SYSTEM IN ILLINOIS 205 (Andrew A. Bruce et al. eds., 1928) (discussing factors that may help determine whether an individual is more or less likely to violate his or her parole).
-
See generally Ernest W. Burgess, Factors Determining Success or Failure on Parole, in THE WORKINGS OF THE INDETERMINATE-SENTENCE LAW AND THE PAROLE SYSTEM IN ILLINOIS 205 (Andrew A. Bruce et al. eds., 1928) (discussing factors that may help determine whether an individual is more or less likely to violate his or her parole).
-
-
-
-
64
-
-
33947631853
-
-
See generally LLOYD E. OHLIN, SELECTION FOR PAROLE: A MANUAL OF PAROLE PREDICTION (1951) (discussing factors that may help determine whether an individual is likely to violate his or her parole and how to utilize these factors).
-
See generally LLOYD E. OHLIN, SELECTION FOR PAROLE: A MANUAL OF PAROLE PREDICTION (1951) (discussing factors that may help determine whether an individual is likely to violate his or her parole and how to utilize these factors).
-
-
-
-
66
-
-
33947699299
-
-
David P. Farrington & Roger Tarling, Criminological Prediction: An Introduction, in PREDICTION IN CRIMINOLOGY, supra note 5, at 7 (citing STEVEN GLUECK & ELEANOR T. GLUECK, UNRAVELING JUVENILE DELINQUENCY (1950)).
-
David P. Farrington & Roger Tarling, Criminological Prediction: An Introduction, in PREDICTION IN CRIMINOLOGY, supra note 5, at 7 (citing STEVEN GLUECK & ELEANOR T. GLUECK, UNRAVELING JUVENILE DELINQUENCY (1950)).
-
-
-
-
67
-
-
33947711701
-
-
Farrington & Tarling, supra note 52, at 8
-
Farrington & Tarling, supra note 52, at 8.
-
-
-
-
68
-
-
33947690672
-
-
For a discussion of potential predictors typically used in a juvenile court or welfare agency, see generally GWEN A. KUTZ & LOUIS E. MOORE, THE 8% PROBLEM: CHRONIC JUVENILE OFFENDER RECIDIVISM (1994);
-
For a discussion of potential predictors typically used in a juvenile court or welfare agency, see generally GWEN A. KUTZ & LOUIS E. MOORE, THE "8% PROBLEM": CHRONIC JUVENILE OFFENDER RECIDIVISM (1994);
-
-
-
-
69
-
-
0040304760
-
-
Charles W. Dean et al., Criminal Propensities, Discrete Groups of Offenders, and Persistence in Crime, 34 CRIMINOLOGY 547 (19%); Christy A. Visher et al., Predicting the Recidivism of Serious Youthful Offenders Using Survival Models, 29 CRIMINOLOGY 329 (1991).
-
Charles W. Dean et al., Criminal Propensities, Discrete Groups of Offenders, and Persistence in Crime, 34 CRIMINOLOGY 547 (19%); Christy A. Visher et al., Predicting the Recidivism of Serious Youthful Offenders Using Survival Models, 29 CRIMINOLOGY 329 (1991).
-
-
-
-
70
-
-
0019349390
-
-
See Sheila Mitchell & Peter Rosa, Boyhood Behaviour Problems as Precursors of Criminality: A Fifteen-Year Follow-Up Study, 22 J. CHILD PSYCHOL. & PSYCHIATRY & ALLIED DISCIPLINES 19, 33 (1981) (finding that a child is more likely to be convicted of crime later in life if he or she has previously exhibited anti-social behavior (stealing, lying, destructiveness and wandering from home)).
-
See Sheila Mitchell & Peter Rosa, Boyhood Behaviour Problems as Precursors of Criminality: A Fifteen-Year Follow-Up Study, 22 J. CHILD PSYCHOL. & PSYCHIATRY & ALLIED DISCIPLINES 19, 33 (1981) (finding that a child is more likely to be convicted of crime later in life if he or she has previously exhibited "anti-social behavior (stealing, lying, destructiveness and wandering from home)").
-
-
-
-
71
-
-
33947711700
-
-
See DAVID RILEY & MARGARET SHAW, HOME OFFICE RESEARCH STUDY NO. 83, PARENTAL SUPERVISION AND JUVENILE DELINQUENCY 1, 2 (1985) (noting that child offenders tend to come from large families, to have parents with a criminal record, to have poor or erratic discipline at home, conflict between parents, and poor supervision by their parents).
-
See DAVID RILEY & MARGARET SHAW, HOME OFFICE RESEARCH STUDY NO. 83, PARENTAL SUPERVISION AND JUVENILE DELINQUENCY 1, 2 (1985) (noting that child offenders "tend to come from large families, to have parents with a criminal record, to have poor or erratic discipline at home, conflict between parents, and poor supervision by their parents").
-
-
-
-
72
-
-
33947614631
-
-
See DONALD J. WEST, DELINQUENCY: ITS ROOTS, CAREERS & PROSPECTS 29-30 (1982) (finding that juveniles are twice as likely to become delinquent if their parents have a low income, come from a large family, have a criminal record, or are unsatisfactory with below-average intelligence).
-
See DONALD J. WEST, DELINQUENCY: ITS ROOTS, CAREERS & PROSPECTS 29-30 (1982) (finding that juveniles are twice as likely to become delinquent if their parents have a low income, come from a large family, have a criminal record, or are "unsatisfactory" with "below-average intelligence").
-
-
-
-
73
-
-
33947680678
-
-
See DAVID P. FARRINGTON, NAT'L INST. OF JUSTICE, FURTHER ANALYSES OF A LONGITUDINAL SURVEY OF CRIME AND DELINQUENCY: FINAL REPORT TO THE NATIONAL INSTITUTE OF JUSTICE 68-80 (1983) (discussing correlation between troubled youths and families with convicted parents).
-
See DAVID P. FARRINGTON, NAT'L INST. OF JUSTICE, FURTHER ANALYSES OF A LONGITUDINAL SURVEY OF CRIME AND DELINQUENCY: FINAL REPORT TO THE NATIONAL INSTITUTE OF JUSTICE 68-80 (1983) (discussing correlation between troubled youths and families with convicted parents).
-
-
-
-
74
-
-
33947648932
-
-
Albert I. Reiss, Jr., Co-Offender Influences on Criminal Careers, in 2 CRIMINAL CAREERS AND CAREER CRIMINALS, supra note 28, at 122-23.
-
Albert I. Reiss, Jr., Co-Offender Influences on Criminal Careers, in 2 CRIMINAL CAREERS AND "CAREER CRIMINALS," supra note 28, at 122-23.
-
-
-
-
75
-
-
33947699832
-
-
See MICHAEL R. GOTTFREDSON, HOME OFFICE RESEARCH STUDY NO. 81, VICTIMS OF CRIME: THE DIMENSIONS OF RISK 14-15 (1984) (finding that self-reported drinking correlates with risk of personal victimization).
-
See MICHAEL R. GOTTFREDSON, HOME OFFICE RESEARCH STUDY NO. 81, VICTIMS OF CRIME: THE DIMENSIONS OF RISK 14-15 (1984) (finding that self-reported drinking correlates with risk of personal victimization).
-
-
-
-
76
-
-
33947634996
-
-
See WILLIAM MCCORD & JOAN MCCORD, THE PSYCHOPATH: AN ESSAY ON THE CRIMINAL MIND 8-17 (1964) (discussing personality traits that can characterize individual as psychopath, including being asocial, having uncontrollable desires, being impulsive, being aggressive, feeling little guilt, and not having capacity to develop lasting, loving relationships).
-
See WILLIAM MCCORD & JOAN MCCORD, THE PSYCHOPATH: AN ESSAY ON THE CRIMINAL MIND 8-17 (1964) (discussing personality traits that can characterize individual as psychopath, including being asocial, having uncontrollable desires, being impulsive, being aggressive, feeling little guilt, and not having capacity to develop lasting, loving relationships).
-
-
-
-
77
-
-
4644237414
-
-
See David Thornton et al., Pretreatment Self-Esteem and Posttreatment Sexual Recidivism, 48 INT'L J. OFFENDER THERAPY & COMP. CRIMINOLOGY 587, 587 (2004) (finding a correlation between low self-esteem and higher sexual recidivism).
-
See David Thornton et al., Pretreatment Self-Esteem and Posttreatment Sexual Recidivism, 48 INT'L J. OFFENDER THERAPY & COMP. CRIMINOLOGY 587, 587 (2004) (finding a correlation between low self-esteem and higher sexual recidivism).
-
-
-
-
78
-
-
33947687124
-
-
See Andrews et al, supra note 27, at 7 stating that treatment strategies for juvenile criminals should be matched to motivation of participants
-
See Andrews et al., supra note 27, at 7 (stating that treatment strategies for juvenile criminals should be matched to motivation of participants).
-
-
-
-
79
-
-
0023199175
-
-
See Stephen A. Cernkovich & Peggy C. Giordano, Family Relationships and Delinquency, 25 CRIMINOLOGY 295, 297 (1987) (discussing that children in homes in which family members get along well with one another are less likely to have delinquency problems, even if parents are separated or divorced).
-
See Stephen A. Cernkovich & Peggy C. Giordano, Family Relationships and Delinquency, 25 CRIMINOLOGY 295, 297 (1987) (discussing that children in homes in which family members get along well with one another are less likely to have delinquency problems, even if parents are separated or divorced).
-
-
-
-
80
-
-
84984315075
-
-
See Steven A. Cernkovich & Peggy C Giordano, School Bonding, Race and Delinquency, 30 CRIMINOLOGY 261, 261 (1992) (noting that juvenile delinquency increases when juvenile's bond to school is low).
-
See Steven A. Cernkovich & Peggy C Giordano, School Bonding, Race and Delinquency, 30 CRIMINOLOGY 261, 261 (1992) (noting that juvenile delinquency increases when juvenile's bond to school is low).
-
-
-
-
81
-
-
0027000034
-
-
See Richard Dembo et al., The Role of Family Factors, Physical Abuse and Sexual Victimization Experiences in High-Risk Youths' Alcohol and Other Drug Use and Delinquency: A Longitudinal Model, 7 VIOLENCE & VICTIMS 245, 245 (1992) (finding significant relationship between physical abuse or sexual victimization and juvenile delinquency).
-
See Richard Dembo et al., The Role of Family Factors, Physical Abuse and Sexual Victimization Experiences in High-Risk Youths' Alcohol and Other Drug Use and Delinquency: A Longitudinal Model, 7 VIOLENCE & VICTIMS 245, 245 (1992) (finding significant relationship between physical abuse or sexual victimization and juvenile delinquency).
-
-
-
-
82
-
-
0030899092
-
-
See Pamela K. Lattimore et al., Risk of Death Among Serious Young Offenders, 34 J. RES. CRIME & DELINQ. 187, 188 (1997) (noting that gang involvement, a factor that leads to victimization, may also lead to criminality).
-
See Pamela K. Lattimore et al., Risk of Death Among Serious Young Offenders, 34 J. RES. CRIME & DELINQ. 187, 188 (1997) (noting that gang involvement, a factor that leads to victimization, may also lead to criminality).
-
-
-
-
83
-
-
0036025777
-
-
Jones et al., supra note 24, at 480; Robert J. Sampson et al., Assessing Neighborhood Effects: Social Processes and New Directions in Research, 28 ANN. REV. SOC. 443, 443-45 (2002).
-
Jones et al., supra note 24, at 480; Robert J. Sampson et al., Assessing "Neighborhood Effects": Social Processes and New Directions in Research, 28 ANN. REV. SOC. 443, 443-45 (2002).
-
-
-
-
84
-
-
33947643065
-
-
See HANNAH-MOFFAT & MAURUTTO, supra note 4, § 2.0, at 3 (noting that risk assessments traditionally receiving the most attention stress static historic factors, such as age, number and type of convictions, sexual offending, and relationship to victim).
-
See HANNAH-MOFFAT & MAURUTTO, supra note 4, § 2.0, at 3 (noting that risk assessments traditionally receiving the most attention stress "static historic factors, such as age, number and type of convictions, sexual offending, and relationship to victim").
-
-
-
-
85
-
-
33947654295
-
-
See id. at 2-4 (discussing distinctiveness of third- generation risk assessment that takes into account both static and dynamic risk factors).
-
See id. at 2-4 (discussing distinctiveness of "third- generation risk assessment" that takes into account both static and dynamic risk factors).
-
-
-
-
86
-
-
33947632913
-
-
SIMON, supra note 36, at 154
-
SIMON, supra note 36, at 154.
-
-
-
-
87
-
-
33947659934
-
-
Simon used Predictive Attribute Analysis and OLS Multiple Regression in her work. Id. at 80, 91.
-
Simon used Predictive Attribute Analysis and OLS Multiple Regression in her work. Id. at 80, 91.
-
-
-
-
88
-
-
33947689124
-
-
Tarling & Perry, supra note 5, at 214-26
-
Tarling & Perry, supra note 5, at 214-26.
-
-
-
-
89
-
-
33947711702
-
-
See generally STEVEN D. GOTTFREDSON & MICHAEL D. GOTTFREDSON, SCREENING FOR RISK: A COMPARISON OF METHODS 62-63 (1979) (determining analytic methods examined produced same degree of predictive efficiency).
-
See generally STEVEN D. GOTTFREDSON & MICHAEL D. GOTTFREDSON, SCREENING FOR RISK: A COMPARISON OF METHODS 62-63 (1979) (determining analytic methods examined produced same degree of predictive efficiency).
-
-
-
-
90
-
-
33947711703
-
-
Steven D. Gottfredson & Don M. Gottfredson, Screening for Risk Among Parolees: Policy, Practice and Method, in PREDICTION IN CRIMINOLOGY, supra note 5, at 54, 75;
-
Steven D. Gottfredson & Don M. Gottfredson, Screening for Risk Among Parolees: Policy, Practice and Method, in PREDICTION IN CRIMINOLOGY, supra note 5, at 54, 75;
-
-
-
-
91
-
-
33947658384
-
-
see also DON M. GOTTFREDSON ET AL., THE UTILIZATION OF EXPERIENCE IN PAROLE DECISION-MAKING: SUMMARY REPORT 18 (1974) (suggesting that less powerful and more simple prediction techniques may produce better predictive validity than more complex techniques given insufficient data).
-
see also DON M. GOTTFREDSON ET AL., THE UTILIZATION OF EXPERIENCE IN PAROLE DECISION-MAKING: SUMMARY REPORT 18 (1974) (suggesting that less powerful and more simple prediction techniques may produce better predictive validity than more complex techniques given insufficient data).
-
-
-
-
93
-
-
33947617237
-
-
Wilkins, supra note 5, at 50
-
Wilkins, supra note 5, at 50.
-
-
-
-
94
-
-
33947695129
-
-
S. CHRISTOPHER BAIRD ET AL., WIS. DIV. OF CORR., THE WISCONSIN CASE CLASSIFICATION/STAFF DEPLOYMENT PROJECT: A TWO YEAR FOLLOW-UP REPORT 9 (1979).
-
S. CHRISTOPHER BAIRD ET AL., WIS. DIV. OF CORR., THE WISCONSIN CASE CLASSIFICATION/STAFF DEPLOYMENT PROJECT: A TWO YEAR FOLLOW-UP REPORT 9 (1979).
-
-
-
-
95
-
-
33947696112
-
-
Tarling & Perry, supra note 5, at 215-16
-
Tarling & Perry, supra note 5, at 215-16.
-
-
-
-
96
-
-
33947640492
-
-
See SIMON, supra note 36, at 72-134 comparing several statistical techniques for their predictability
-
See SIMON, supra note 36, at 72-134 (comparing several statistical techniques for their predictability).
-
-
-
-
97
-
-
33947651094
-
-
See Gottfredson & Gottfredson, supra note 75, at 54 comparing statistical efficiency of five methods for predicting parole risk
-
See Gottfredson & Gottfredson, supra note 75, at 54 (comparing statistical efficiency of five methods for predicting parole risk).
-
-
-
-
98
-
-
33947656902
-
-
See Tarling & Perry, supra note 5, at 212-26 studying whether social and criminal histories of individuals were predictive of future criminal convictions using the Automatic Interaction Detection and Logistic Regression methods of analysis
-
See Tarling & Perry, supra note 5, at 212-26 (studying whether social and criminal histories of individuals were predictive of future criminal convictions using the Automatic Interaction Detection and Logistic Regression methods of analysis).
-
-
-
-
99
-
-
33947683266
-
-
Peter R. Jones et al., Evaluating Services to Delinquent Youth in Philadelphia: The ProDES Information System, 59 J. PA. ASS'N PROBATION, PAROLE & CORRECTIONS 10, 10-13 (1999).
-
Peter R. Jones et al., Evaluating Services to Delinquent Youth in Philadelphia: The ProDES Information System, 59 J. PA. ASS'N PROBATION, PAROLE & CORRECTIONS 10, 10-13 (1999).
-
-
-
-
100
-
-
33947704094
-
-
Removal of cases with missing data is not a problem in the present study since its function is solely to compare different methods of assessment for the same juveniles rather than build a risk assessment tool for use in the field
-
Removal of cases with missing data is not a problem in the present study since its function is solely to compare different methods of assessment for the same juveniles rather than build a risk assessment tool for use in the field.
-
-
-
-
101
-
-
33947616192
-
-
Prorated total risk scores were calculated in cases involving any missing data
-
Prorated total risk scores were calculated in cases involving any missing data.
-
-
-
-
102
-
-
33947622557
-
-
Alternative cutoff points were evaluated but none provided improvement
-
Alternative cutoff points were evaluated but none provided improvement.
-
-
-
-
103
-
-
33947633437
-
-
note 78, at tbl.1, 21
-
BAIRD ET AL., supra note 78, at 10 tbl.1, 21.
-
supra
, pp. 10
-
-
ET AL., B.1
-
104
-
-
33947628110
-
-
Id. at 10 tbl.1, 11 tbl.2.
-
Id. at 10 tbl.1, 11 tbl.2.
-
-
-
-
105
-
-
84984357712
-
Universal Applicability of Probation Risk-Assessment Instruments: A Critique, 22
-
K.N. Wright, T.R. Clear & P. Dickinson, Universal Applicability of Probation Risk-Assessment Instruments: A Critique, 22 CRIMINOLOGY 113-34 (1984).
-
(1984)
CRIMINOLOGY
, vol.113 -34
-
-
Wright, K.N.1
Clear, T.R.2
Dickinson, P.3
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106
-
-
33947679718
-
-
Id
-
Id.
-
-
-
-
107
-
-
33947637110
-
-
Gambrill & Shlonsky, supra note 32, at 825-26
-
Gambrill & Shlonsky, supra note 32, at 825-26.
-
-
-
-
108
-
-
33947659933
-
-
Arentze et al., supra note 42, at 336-38. A tree induction algorithm, or nonparametric classification procedure, is a discrete choice model that predicts an individual's behavior and choices. Id. at 330. These algorithms assume that individuals compare a number of choice alternatives on relevant attributes and choose the alternative that maximizes some measure of utility. Id.
-
Arentze et al., supra note 42, at 336-38. A tree induction algorithm, or nonparametric classification procedure, is a "discrete choice model" that predicts an individual's behavior and choices. Id. at 330. These algorithms "assume that individuals compare a number of choice alternatives on relevant attributes and choose the alternative that maximizes some measure of utility." Id.
-
-
-
-
109
-
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0037591625
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Irene Casas, Evaluating the Importance of Accessibility to Congestion Response Using a GIS-Based Travel Simulator, 5 J. GEOGRAPHICAL SYS. 109, 119 (2003).
-
Irene Casas, Evaluating the Importance of Accessibility to Congestion Response Using a GIS-Based Travel Simulator, 5 J. GEOGRAPHICAL SYS. 109, 119 (2003).
-
-
-
-
110
-
-
33947674122
-
-
See R. GNANADESIKAN, METHODS FOR STATISTICAL DATA ANALYSIS OF MULTIVARIATE OBSERVATIONS 333 (1997) (stating that since the late 1970s researchers have widely used the Statistical Package for the Social Scientist to perform data analysis).
-
See R. GNANADESIKAN, METHODS FOR STATISTICAL DATA ANALYSIS OF MULTIVARIATE OBSERVATIONS 333 (1997) (stating that since the late 1970s researchers have widely used the Statistical Package for the Social Scientist to perform data analysis).
-
-
-
-
111
-
-
14644403045
-
-
Emma C. Smith & Klaus Grawe, What Makes Psychotherapy Sessions Productive? A New Approach to Bridging the Gap Between Process Research and Practice, 10 CLINICAL PSYCHOL. & PSYCHOTHERAPY 275, 278 (2003).
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Emma C. Smith & Klaus Grawe, What Makes Psychotherapy Sessions Productive? A New Approach to Bridging the Gap Between Process Research and Practice, 10 CLINICAL PSYCHOL. & PSYCHOTHERAPY 275, 278 (2003).
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See Casas, supra note 93, at 119-24 using the CHAID method of risk assessment research for geographical planning of transportation system
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See Casas, supra note 93, at 119-24 (using the CHAID method of risk assessment research for geographical planning of transportation system).
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116
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Jones et al., supra note 24, at 490.
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Bennie R. Grobler et al., The Chaid-Technique and the Relationship Between School Effectiveness and Various Independent Variables, 30 INT'L STUD. EDUC. ADMIN. 49, 49-50 (2002).
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Jay Magidson, The CHAID Approach to Segmentation Modeling: CHi-squared Automatic Interaction Detection, in ADVANCED METHODS OF MARKETING RESEARCH 118, 156-57 (Richard P. Bagozzi ed., 1994) (discussing that CHAID segmentation algorithm is superior to the previously used models when desired result is clusters with differing criterion).
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Jay Magidson, The CHAID Approach to Segmentation Modeling: CHi-squared Automatic Interaction Detection, in ADVANCED METHODS OF MARKETING RESEARCH 118, 156-57 (Richard P. Bagozzi ed., 1994) (discussing that CHAID segmentation algorithm is superior to the previously used models when desired result is clusters with differing criterion).
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121
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Schwartz et al, supra note 7, at 1082
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Schwartz et al., supra note 7, at 1082.
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Id. at 1091
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EARL COX, FUZZY LOGIC FOR BUSINESS AND INDUSTRY 582 (1995).
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EARL COX, FUZZY LOGIC FOR BUSINESS AND INDUSTRY 582 (1995).
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Schwartz et al, supra note 7, at 1084
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Felipe Atienza et al., Risk Stratification in Heart Failure Using Artificial Neural Networks, PROC. AMIA ANN. SYMP. 32, 34 (2000), available at http://www.amia.org/pubs/proceedings/ symposia/2000/D200367.pdf.
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Felipe Atienza et al., Risk Stratification in Heart Failure Using Artificial Neural Networks, PROC. AMIA ANN. SYMP. 32, 34 (2000), available at http://www.amia.org/pubs/proceedings/ symposia/2000/D200367.pdf.
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Schwartz et al., supra note 7, at 1084.
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128
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For a discussion of Schwartz's work with neural networks, see supra note 7, at 1081
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For a discussion of Schwartz's work with neural networks, see supra note 7, at 1081.
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129
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Schwartz et al, supra note 7, at 1092; see also David B. Marshall & Diana J. English, Neural Network Modeling of Risk Assessment in Child Protective Services, 5 PSYCHOL. METHODS 102, 103 (2000) (concluding that under certain conditions linear decision making is superior to clinical judgments).
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Schwartz et al, supra note 7, at 1092; see also David B. Marshall & Diana J. English, Neural Network Modeling of Risk Assessment in Child Protective Services, 5 PSYCHOL. METHODS 102, 103 (2000) (concluding that under certain conditions linear decision making is superior to clinical judgments).
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NIGRIN, supra note 42, at 107-08.
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NIGRIN, supra note 42, at 107-08.
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131
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See Gottfredson, supra note 5, at 24 suggesting that no clear-cut empirical advantages of selecting one prediction method over another exist
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See Gottfredson, supra note 5, at 24 (suggesting that "no clear-cut empirical advantages of selecting one prediction method over another" exist).
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132
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33947632353
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See SCHMIDT & WITTE, supra note 39, at 15 finding it encouraging that research studies kept false positives and false negatives below fifty percent
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See SCHMIDT & WITTE, supra note 39, at 15 (finding it encouraging that research studies kept false positives and false negatives below fifty percent).
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See, e.g., EILEEN GAMBRILL, CRITICAL THINKING IN CLINICAL PRACTICE: IMPROVING THE QUALITY OF JUDGMENTS & DECISIONS 442-43 (2d ed. 2005) (stating that actuarial model is superior because it can better integrate substantial amounts of diverse data).
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See, e.g., EILEEN GAMBRILL, CRITICAL THINKING IN CLINICAL PRACTICE: IMPROVING THE QUALITY OF JUDGMENTS & DECISIONS 442-43 (2d ed. 2005) (stating that actuarial model is superior because it can better integrate substantial amounts of diverse data).
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135
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33947664929
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CANADIAN HUMAN RIGHTS COMM'N, PROTECTING THEIR RIGHTS: A SYSTEMIC REVIEW OF HUMAN RIGHTS IN CORRECTIONAL SERVICES FOR FEDERALLY SENTENCED WOMEN 24-25 (2003).
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CANADIAN HUMAN RIGHTS COMM'N, PROTECTING THEIR RIGHTS: A SYSTEMIC REVIEW OF HUMAN RIGHTS IN CORRECTIONAL SERVICES FOR FEDERALLY SENTENCED WOMEN 24-25 (2003).
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136
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See Zinger, supra note 25, at 610 citing series of criticisms of actuarial tools for discrimination against women
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See Zinger, supra note 25, at 610 (citing series of criticisms of actuarial tools for discrimination against women).
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137
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33947626545
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Id. at 613
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Id. at 613.
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138
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33947678140
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Id. at 613-14 (quoting Kelly Hannah-Moffat & Margaret Shaw, Situation Risquee: Le Risque et les Services Correctionnels au Canada, 34 CRIMINOLOGIE 47 (2001)).
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Id. at 613-14 (quoting Kelly Hannah-Moffat & Margaret Shaw, Situation Risquee: Le Risque et les Services Correctionnels au Canada, 34 CRIMINOLOGIE 47 (2001)).
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139
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33947644777
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David P. Cole & Glenn Angus, Using Pre-Sentence Reports to Evaluate and Respond to Risk, 47 CRIM. L.Q. 302, 313-14 (2003) (citing, among other cases. Bains v. Canada (Nat'l Parole Bd.), [1989] 3 F.C. 450; Moore v. Valdez, unreported op. (Fla. C.I. Aug. 21, 2000)).
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David P. Cole & Glenn Angus, Using Pre-Sentence Reports to Evaluate and Respond to Risk, 47 CRIM. L.Q. 302, 313-14 (2003) (citing, among other cases. Bains v. Canada (Nat'l Parole Bd.), [1989] 3 F.C. 450; Moore v. Valdez, unreported op. (Fla. C.I. Aug. 21, 2000)).
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140
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Zinger, supra note 25, at 615
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Zinger, supra note 25, at 615.
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141
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33947682167
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In re Commitment of Simons, 821 N.E.2d 1184, 1186 (Ill. 2004).
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In re Commitment of Simons, 821 N.E.2d 1184, 1186 (Ill. 2004).
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142
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33947690673
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Simons, 821 N.E.2d at 1186. Frye provides that scientific evidence is an admissible methodology if it has gained mainstream, general acceptance. Frye v. United States, 293 F. 1013, 1014 (D.C. Cir. 1923).
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Simons, 821 N.E.2d at 1186. Frye provides that scientific evidence is an admissible methodology if it has gained mainstream, general acceptance. Frye v. United States, 293 F. 1013, 1014 (D.C. Cir. 1923).
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143
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Simons, 821 N.E.2d at 1186-87.
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Simons, 821 N.E.2d at 1186-87.
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144
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Id. at 1196
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Id. at 1196.
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145
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Zinger, supra note 25, at 615-16
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Zinger, supra note 25, at 615-16.
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146
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33947673854
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See supra Part III.E for a comparison of the three models discussed.
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See supra Part III.E for a comparison of the three models discussed.
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147
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As Ivan Zinger notes, risk assessment has become big business and independent evaluations are required to support those of the individuals or companies that developed the tools. Zinger, supra note 25, at 608
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As Ivan Zinger notes, risk assessment has become big business and independent evaluations are required to support those of the individuals or companies that developed the tools. Zinger, supra note 25, at 608.
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