Forecasting Murder Within a Population of Probationers and Parolees: A High Stakes Application of Statistical Learning
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Summary: Forecasts of future dangerousness are often used to inform the sentencing decisions of convicted offenders. For individuals who are sentenced to probation or paroled to community supervision, such forecasts affect the conditions under which they are to be supervised. The statistical criterion for these forecasts is commonly called recidivism, which is defined as a charge or conviction for any new offence, no matter how minor. Only rarely do such forecasts make distinctions on the basis of the seriousness of offences. Yet seriousness may be central to public concerns, and judges are increasingly required by law and sentencing guidelines to make assessments of seriousness. At the very least, information about seriousness is essential for allocating scarce resources for community supervision of convicted offenders. The paper focuses only on murderous conduct by individuals on probation or parole. Using data on a population of over 60000 cases from Philadelphia’s Adult Probation and Parole Department, we forecast whether each offender will be charged with a homicide or attempted homicide within 2 years of beginning community supervision. We use a statistical learning approach that makes no assumptions about how predictors are related to the outcome. We also build in the costs of false negative and false positive charges and use half of the data to build the forecasting model, and the other half of the data to evaluate the quality of the forecasts. Forecasts that are based on this approach offer the possibility of concentrating rehabilitation, treatment and surveillance resources on a small subset of convicted offenders who may be in greatest need, and who pose the greatest risk to society.
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Berk et al. evaluated their model only on the set of defendants who had been granted parole.
“For instance, Berk et al. evaluated their model only on the set of defendants who have been granted parole.”
From The Selective Labels Problem (2017) · cites this paper as [7]
The Setting (p. unavailable) describes the supervised population as both probationers and parolees. The Data section (p. unavailable) says the test sample was drawn from APPD intake cases, not solely from people granted parole. (LLM, conf=0.90)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredThe assumption of no unmeasured confounders does not typically hold in cases where human decisions are providing data labels.
“This assumption does not typically hold in cases where human decisions are providing data labels [7, 21].”
From The Selective Labels Problem (2017) · cites this paper as [7]
The Setting (section 3; page unavailable) says homicide charges are an imperfect outcome measure and that convictions introduce further error through adjudication. The Discussion (section 6; page unavailable) also notes that subjective forecasts may contain unmeasured racial bias. These passages support concerns about human decisions in the data, but the paper does not say that the no-unmeasured-confounders assumption typically fails when humans provide labels. (LLM, conf=0.77)
Experimental — accuracy not yet measuredPrior research often overlooks the selective labels problem, evaluating models by computing traditional metrics such as AUC or accuracy only on data points for which ground truth labels are available.
“While prior research has explored applications of machine learning to domains where human judgments are part of the data labeling process (crime [7, 21, 43], medical diagnosis [6, 22]), the selective labels problem is often overlooked, with evaluation of the models typically carried out by computing traditional metrics such as AUC or accuracy only on the data points for which ground truth labels”
From The Selective Labels Problem (2017) · cites this paper as [7]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,834 · see citing works
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,028 · see citing works
Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325 · see citing works
Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy
Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997) · Science · doi:10.1126/science.277.5328.918 · Cited by 11,999 · see citing works
Friedman, J. H. (2002) · Computational Statistics & Data Analysis · doi:10.1016/s0167-9473(01)00065-2 · Cited by 7,067 · see citing works
The Elements of Statistical Learning
Hastie, T. J., Tibshirani, R. J., & Friedman, J. H. (2009) · Springer series in statistics · doi:10.1007/b94608 · Cited by 4,111 · see citing works
Bias in random forest variable importance measures: Illustrations, sources and a solution
Strobl, C., Boulesteix, A., Zeileis, A., & Hothorn, T. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-25 · Cited by 3,755 · see citing works
Criminal Careers and "Career Criminals,"
National Research Council (1986) · National Academies Press eBooks · doi:10.17226/922 · Cited by 1,336 · see citing works
Fairness and Effectiveness in Policing: The Evidence
Anderson, M. C., & Giles, H. (2005) · Journal of Communication · doi:10.1111/j.1460-2466.2005.tb03028.x · Cited by 795 · see citing works
Clinical versus Statistical Prediction.
Sletto, R. F., & Meehl, P. E. (1955) · American Sociological Review · doi:10.2307/2092752 · Cited by 636 · see citing works
Fairness and Effectiveness in Policing : The Evidence
Practices, Skogan, W. G., & Frydl, K. J. (2004) · Medical Entomology and Zoology · Cited by 635 · see citing works
The Seriousness of Crimes: Normative Structure and Individual Differences
Rossi, P. H., Waite, E., Bose, C. E., & Berk, R. E. (1974) · American Sociological Review · doi:10.2307/2094234 · Cited by 572 · see citing works
Self-citationRandom Forests and Adaptive Nearest Neighbors
Lin, Y., & Jeon, Y. (2006) · Journal of the American Statistical Association · doi:10.1198/016214505000001230 · Cited by 507 · see citing works
Recursive Partitioning in the Health Sciences
Zhang, H., & Singer, B. H. (1999) · Statistics in the health sciences · doi:10.1007/978-1-4757-3027-2 · Cited by 455 · see citing works
Statistical Learning from a Regression Perspective
Berk, R. A. (2016) · Springer texts in statistics · doi:10.1007/978-3-319-44048-4 · Cited by 356 · see citing works
Self-citationStatistical Risk Assessment: Old Problems and New Applications
Gottfredson, S. D., & Moriarty, L. J. (2005) · Crime & Delinquency · doi:10.1177/0011128705281748 · Cited by 233 · see citing works
Bonta, J. (2002) · Criminal Justice and Behavior · doi:10.1177/0093854802029004002 · Cited by 214 · see citing works
Predicting recidivism using survival models
(1989) · Journal of Criminal Justice · doi:10.1016/0047-2352(89)90041-x · Cited by 198 · see citing works
Sherman, L. W. (2007) · Journal of Experimental Criminology · doi:10.1007/s11292-007-9044-y · Cited by 183 · see citing works
Self-citationAn Introduction to Ensemble Methods for Data Analysis
Berk, R. A. (2006) · Sociological Methods & Research · doi:10.1177/0049124105283119 · Cited by 170 · see citing works
Self-citationPredicting Recidivism Using Survival Models
Schmidt, P., & Witte, A. D. (1988) · Research in criminology · doi:10.1007/978-1-4612-3772-3 · Cited by 155 · see citing works
Weiner, N. A., Farrington, D. P., & Tarling, R. (1986) · The Journal of Criminal Law and Criminology (1973-) · doi:10.2307/1143604 · Cited by 148 · see citing works
Clinical versus Statistical Prediction
Twedt, D. W., & Meehl, P. E. (1956) · Journal of Marketing · doi:10.2307/1247883 · Cited by 97 · see citing works
Crime and the Politics of Hysteria: How the Willie Horton Story Changed American Justice
Anderson, D. C., & Enberg, C. (1995) · Journal of Contemporary Criminal Justice · doi:10.1177/104398629501100408 · Cited by 91 · see citing works
Forecasting Dangerous Inmate Misconduct: An Application of Ensemble Statistical Procedures
Berk, R. A., Kriegler, B., & Baek, J.-H. (2006) · Journal of Quantitative Criminology · doi:10.1007/s10940-006-9005-z · Cited by 82 · see citing works
Self-citationStatistical Learning from a Regression Perspective
Shalabh (2009) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2009.00614_2.x · Cited by 75 · see citing works
Predicting Individual Crime Rates
Farrington, D. P. (1987) · Crime and Justice · doi:10.1086/449132 · Cited by 70 · see citing works
Forecasting Methods in Crime and Justice
Berk, R. A. (2008) · Annual Review of Law and Social Science · doi:10.1146/annurev.lawsocsci.3.081806.112812 · Cited by 57 · see citing works
Self-citationPrediction: An Overview of Selected Methodological Issues
Gottfredson, S. D. (1987) · Crime and Justice · doi:10.1086/449131 · Cited by 56 · see citing works
The Efficiency of Prediction in Criminology
Ohlin, L. E., & Duncan, O. D. (1949) · American Journal of Sociology · doi:10.1086/220398 · Cited by 45 · see citing works
Developing a Practical Forecasting Screener for Domestic Violence Incidents
Berk, R. A., He, Y., & Sorenson, S. B. (2005) · Evaluation Review · doi:10.1177/0193841x05275333 · Cited by 43 · see citing works
Self-citationTHE IMPACT OF MEDICAL RESOURCES ON CRIMINALLY INDUCED LETHALITY: A FURTHER EXAMINATION*
Doerner, W. G. (1988) · Criminology · doi:10.1111/j.1745-9125.1988.tb00837.x · Cited by 34 · see citing works
The Accuracy, Efficiency, and Validity of a Prediction Instrument
Reiss, A. J. J. (1951) · American Journal of Sociology · doi:10.1086/220818 · Cited by 31 · see citing works
Factors for Predicting Parole Success
Borden, H. G. (1928) · Journal of the American Institute of Criminal Law and Criminology · doi:10.2307/1134622 · Cited by 30 · see citing works
Bias in Random Forest Variable Importance Measures
Strobl, C., Boulesteix, A., Zeileis, A., & Hothorn, T. (2006) · Cited by 26 · see citing works
Binary Segmentation in Survey Analysis with Particular Reference to AID
Fielding, A., & O’Muircheartaigh, C. A. (1977) · Journal of the Royal Statistical Society Series D (The Statistician) · doi:10.2307/2988216 · Cited by 24 · see citing works
VIRGINIA CRIMINAL SENTENCING COMMISSION
DePasquale, J., & Farrar-Owens, M. (1997) · Cited by 22 · see citing works
Tutz, G. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9780511842061.016 · Cited by 17 · see citing works
Problems in Parole Prediction: A Historical Analysis
Dean, C. W., & Duggan, T. J. (1968) · Social Problems · doi:10.2307/799833 · Cited by 16 · see citing works
National Crime Victimization Survey: Criminal Victimization, 2005
Catalano, S. (2006) · PsycEXTRA Dataset · doi:10.1037/e563152006-001 · Cited by 12 · see citing works
Sutherland, R. A., & PARTIS, M. D. (1992) · Computer applications in the biosciences · doi:10.1093/bioinformatics/8.1.1 · Cited by 11 · see citing works
Lennert‐Cody, C. E., & Berk, R. A. (2007) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2006.00460.x · Cited by 10 · see citing works
Self-citationGoodman, L. A. (1953) · American Journal of Sociology · doi:10.1086/221203 · Cited by 8 · see citing works
A Comparison of Alternative Methods of Parole Prediction
Ohlin, L. E., & Lawrence, R. A. (1952) · American Sociological Review · doi:10.2307/2088072 · Cited by 8 · see citing works
Generalizing the Problem of Prediction
Goodman, L. A. (1952) · American Sociological Review · doi:10.2307/2088228 · Cited by 6 · see citing works
USE PROBATION TO PREVENT MURDER*
Sherman, L. W. (2007) · Criminology & Public Policy · doi:10.1111/j.1745-9133.2007.00461.x · Cited by 5 · see citing works
Self-citationII. The Validation of Prediction
Goodman, L. A. (1953) · American Journal of Sociology · doi:10.1086/221204 · Cited by 5 · see citing works
Flagged first, then by citations
1 of the 51 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- Burgess, E. W. (1928) Factors determining success or failure on parole. In The Working of the Indeterminant Sentence Law and the Parole System in Illinois (eds A. A. Bruce, A. J. Harno, E. W. Burgess and J. Landesco), pp. 205-249. Springfield: State Board of Parole.
- Maltz, M. D. (1984) Recidvism. New York: Academic Press.
Cited by
Sorted by citations
Algorithmic Decision Making and the Cost of Fairness
Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098095 · Cited by 1,149 · see citing works
The Rise of Evidence-Based Policing: Targeting, Testing, and Tracking
Sherman, L. W. (2013) · Crime and Justice · doi:10.1086/670819 · Cited by 582 · see citing works
Self-citationDurlauf, S. N., & Nagin, D. S. (2011) · Criminology & Public Policy · doi:10.1111/j.1745-9133.2010.00680.x · Cited by 404 · see citing works
Neurocriminology: implications for the punishment, prediction and prevention of criminal behaviour
Glenn, A. L., & Raine, A. (2013) · Nature reviews. Neuroscience · doi:10.1038/nrn3640 · Cited by 317 · see citing works
Interpretation of nonlinear relationships between process variables by use of random forests
Auret, L., & Aldrich, C. (2012) · Minerals Engineering · doi:10.1016/j.mineng.2012.05.008 · Cited by 270 · see citing works
Youth violence: What we know and what we need to know.
Bushman, B. J., Newman, K. S., Calvert, S. L., Downey, G., Dredze, M. H., Gottfredson, M. R., Jablonski, N. G., Masten, A. S., Morrill, C., Neill, D. B., et al. (2016) · American Psychologist · doi:10.1037/a0039687 · Cited by 225 · see citing works
Saunders, J., Hunt, P., & Hollywood, J. S. (2016) · Journal of Experimental Criminology · doi:10.1007/s11292-016-9272-0 · Cited by 193 · see citing works
Sherman, L. W. (2007) · Journal of Experimental Criminology · doi:10.1007/s11292-007-9044-y · Cited by 183 · see citing works
Self-citationStatistical Procedures for Forecasting Criminal Behavior
Berk, R. A., & Bleich, J. (2013) · Criminology & Public Policy · doi:10.1111/1745-9133.12047 · Cited by 180 · see citing works
Self-citationActuarial Sentencing: An “Unsettled” Proposition
Hannah‐Moffat, K. (2012) · Justice Quarterly · doi:10.1080/07418825.2012.682603 · Cited by 166 · see citing works
Interpretable classification models for recidivism prediction
Zeng, J., Ustun, B., & Rudin, C. (2017) · Journal of the Royal Statistical Society Series A: Statistics in Society · doi:10.1111/rssa.12227 · Cited by 220 · see citing works
Johndrow, J. E., & Lum, K. (2019) · The Annals of Applied Statistics · doi:10.1214/18-aoas1201 · Cited by 141 · see citing works
Martini, C. H., Boon, M., Broens, S. J. L., Hekkelman, E. F., Oudhoff, L. A., Buddeke, A. W., & Dahan, A. (2015) · Anesthesiology · doi:10.1097/aln.0000000000000757 · Cited by 140 · see citing works
Forecasting Domestic Violence: A Machine Learning Approach to Help Inform Arraignment Decisions
Berk, R. A., Sorenson, S. B., & Barnes, G. C. (2016) · Journal of Empirical Legal Studies · doi:10.1111/jels.12098 · Cited by 133 · see citing works
Self-citationLearning to Detect Patterns of Crime
Wang, T., Rudin, C. D., Wägner, D., & Sevieri, R. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40994-3_33 · Cited by 126 · see citing works
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Sherman, L. W. (2018) · Annual Review of Criminology · doi:10.1146/annurev-criminol-032317-092409 · Cited by 120 · see citing works
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Lakkaraju, H., Kleinberg, J., Leskovec, J., Ludwig, J., & Mullainathan, S. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098066 · Cited by 110 · see citing works
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Au, Q., Herbinger, J., Stachl, C., Bischl, B., & Casalicchio, G. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00840-5 · Cited by 83 · see citing works
Travaini, G. V., Pacchioni, F., Bellumore, S., Bosia, M., & Micco, F. D. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191710594 · Cited by 82 · see citing works
Berk, R. A., Barnes, G. C., Ahlman, L., & Kurtz, E. (2010) · Journal of Experimental Criminology · doi:10.1007/s11292-010-9095-3 · Cited by 78 · see citing works
Self-citationBrakel, R. E. V., & Hert, P. D. (2011) · VUBIR (Vrije Universiteit Brussel) · Cited by 75 · see citing works
Duwe, G. (2013) · Criminal Justice Policy Review · doi:10.1177/0887403413478821 · Cited by 68 · see citing works
Predicting Recidivism in Homicide Offenders Using Classification Tree Analysis
Neuilly, M.-A., Zgoba, K. M., Tita, G., & Lee, S. S. (2011) · Homicide Studies · doi:10.1177/1088767911406867 · Cited by 59 · see citing works
Using Logistic Regression Modeling to Predict Sexual Recidivism
Duwe, G., & Freske, P. (2012) · Sexual Abuse · doi:10.1177/1079063211429470 · Cited by 59 · see citing works
Duwe, G., & Kim, K. (2015) · Criminal Justice Policy Review · doi:10.1177/0887403415604899 · Cited by 59 · see citing works
Forecasting Methods in Crime and Justice
Berk, R. A. (2008) · Annual Review of Law and Social Science · doi:10.1146/annurev.lawsocsci.3.081806.112812 · Cited by 57 · see citing works
Self-citationTargeting Escalation of Intimate Partner Violence: Evidence from 52,000 Offenders
Barnham, L., Barnes, G. C., & Sherman, L. W. (2017) · Cambridge Journal of Evidence-Based Policing · doi:10.1007/s41887-017-0008-9 · Cited by 56 · see citing works
Self-citationPredicting and Preventing Gun Violence: An Experimental Evaluation of READI Chicago
Bhatt, M., Heller, S., Kapustin, M., Bertrand, M., & Blattman, C. (2023) · The Quarterly Journal of Economics · doi:10.1093/qje/qjad031 · Cited by 55 · see citing works
Low-intensity community supervision for low-risk offenders: a randomized, controlled trial
Barnes, G., Ahlman, L., Gill, C., Sherman, L., Kurtz, E., & Malvestuto, R. (2010) · Journal of Experimental Criminology · doi:10.1007/s11292-010-9094-4 · Cited by 62 · see citing works
Self-citationPolicing Domestic Violence 1967–2017
Sherman, L. W. (2018) · Criminology & Public Policy · doi:10.1111/1745-9133.12365 · Cited by 54 · see citing works
Self-citationThe problem of success: What is a ‘good’ homicide investigation?
Brookman, F., & Innes, M. (2013) · Policing & Society · doi:10.1080/10439463.2013.771538 · Cited by 53 · see citing works
Homicide offender recidivism: A review of the literature
Liem, M. C. A. (2012) · Aggression and Violent Behavior · doi:10.1016/j.avb.2012.08.001 · Cited by 53 · see citing works
Berk, R. A. (2013) · Security Informatics · doi:10.1186/2190-8532-2-5 · Cited by 52 · see citing works
Self-citationVittes, K. A., Vernick, J. S., & Webster, D. W. (2012) · Injury Prevention · doi:10.1136/injuryprev-2011-040290 · Cited by 52 · see citing works
Asymmetric Loss Functions for Forecasting in Criminal Justice Settings
Berk, R. A. (2010) · Journal of Quantitative Criminology · doi:10.1007/s10940-010-9098-2 · Cited by 51 · see citing works
Self-citationDuwe, G. (2018) · Journal of Experimental Criminology · doi:10.1007/s11292-018-9325-7 · Cited by 47 · see citing works
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Dudfield, G., Angel, C. M., Sherman, L. W., & Torrence, S. (2017) · Cambridge Journal of Evidence-Based Policing · doi:10.1007/s41887-017-0001-3 · Cited by 39 · see citing works
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Self-citationBarnes, G. C., Hyatt, J. M., Ahlman, L., & Kent, D. T. L. (2012) · Journal of Crime and Justice · doi:10.1080/0735648x.2012.679874 · Cited by 37 · see citing works
Self-citationBarnes, G. C., Hyatt, J. M., & Sherman, L. W. (2016) · Criminal Justice and Behavior · doi:10.1177/0093854816673862 · Cited by 36 · see citing works
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Rothschild‐Elyassi, G., Koehler, J., & Simon, J. S. (2018) · doi:10.1002/9781119372394.ch14 · Cited by 30 · see citing works
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Roman, C. G., Klein, H. J., & Wolff, K. T. (2017) · Journal of Experimental Criminology · doi:10.1007/s11292-017-9308-0 · Cited by 26 · see citing works
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Schrodt, P. A., Yonamine, J. E., & Bagozzi, B. E. (2012) · Springer eBooks · doi:10.1007/978-1-4614-5311-6_7 · Cited by 25 · see citing works
Loeffler, C. E., & Grunwald, B. (2015) · Journal of Research in Crime and Delinquency · doi:10.1177/0022427815581858 · Cited by 25 · see citing works
Predicting and Preventing Gun Violence: An Experimental Evaluation of READI Chicago
Bhatt, M., Heller, S., Kapustin, M., Bertrand, M., & Blattman, C. (2023) · National Bureau of Economic Research · doi:10.3386/w30852 · Cited by 24 · see citing works
Learning to rank spatio-temporal event hotspots
Mohler, G., Porter, M. D., Carter, J. G., & Lafree, G. D. (2020) · Crime Science · doi:10.1186/s40163-020-00112-x · Cited by 22 · see citing works
Duwe, G., & Kim, K. (2016) · Corrections · doi:10.1080/23774657.2016.1178083 · Cited by 21 · see citing works
Sentinel Events Preceding Youth Firearm Violence
Sumner, S. A., Maenner, M. J., Socias‐Morales, C., Mercy, J. A., Silverman, P., Medinilla, S. P., Martin, S. S., Xu, L., & Hillis, S. D. (2016) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2016.08.002 · Cited by 20 · see citing works
Predicting First-Time Sexual Offending Among Prisoners Without a Prior Sex Offense History
Duwe, G. (2012) · Criminal Justice and Behavior · doi:10.1177/0093854812453911 · Cited by 20 · see citing works
Hu, X., Zhang, X., & Lovrich, N. P. (2020) · Victims & Offenders · doi:10.1080/15564886.2020.1806161 · Cited by 19 · see citing works
Qiu, T., Wang, S., Hu, D., Feng, N., & Cui, L. (2024) · Behavioral Sciences · doi:10.3390/bs14010073 · Cited by 22 · see citing works
Oh, G., Song, J., Park, H., & Na, C. (2021) · Deviant Behavior · doi:10.1080/01639625.2021.1953360 · Cited by 19 · see citing works
D’Alessio, S. J., Stolzenberg, L., & Eitle, D. J. (2013) · American Journal of Criminal Justice · doi:10.1007/s12103-013-9199-1 · Cited by 19 · see citing works
Detecting Gunshots Using Wearable Accelerometers
Loeffler, C. E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106664 · Cited by 18 · see citing works
Huey, L. (2016) · Journal of Community Safety and Well-Being · doi:10.35502/jcswb.15 · Cited by 20 · see citing works
An Introduction to Statistical Learning from a Regression Perspective
Berk, R. A. (2009) · Springer eBooks · doi:10.1007/978-0-387-77650-7_34 · Cited by 17 · see citing works
Self-citationAriel, B. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108278423.022 · Cited by 17 · see citing works
The Influence of Criminal History on the Likelihood of Committing Lethal Versus Nonlethal Violence
Ganpat, S. M., Liem, M. C. A., van der Leun, J., & Nieuwbeerta, P. (2012) · Homicide Studies · doi:10.1177/1088767912466082 · Cited by 17 · see citing works
Harm-focused offender triage and prioritization: a Philadelphia case study
Ratcliffe, J., & Kikuchi, G. (2019) · Policing: An International Journal · doi:10.1108/pijpsm-08-2018-0118 · Cited by 15 · see citing works
A machine learning analysis of misconduct in the New York Police Department
Cubitt, T. I. C., & Birch, P. (2021) · Policing An International Journal · doi:10.1108/pijpsm-11-2020-0178 · Cited by 15 · see citing works
Identifying high‐risk firearm owners to prevent mass violence
Laqueur, H. S., & Wintemute, G. J. (2019) · Criminology & Public Policy · doi:10.1111/1745-9133.12477 · Cited by 15 · see citing works
On LPG usage in rural Vietnamese households
Vahlne, N. (2016) · Development Engineering · doi:10.1016/j.deveng.2016.12.003 · Cited by 15 · see citing works
Investigations: What Could, and Should, Be Taught?
Carson, D. (2013) · The Police Journal Theory Practice and Principles · doi:10.1350/pojo.2013.86.3.628 · Cited by 14 · see citing works
Forecasting and Criminal Justice Policy and Practice
Sabol, W. J., & Baumann, M. L. (2022) · American Journal of Criminal Justice · doi:10.1007/s12103-022-09715-3 · Cited by 16 · see citing works
The recidivism of homicide offenders in Western Australia
Broadhurst, R., Maller, R., Maller, M., & Bouhours, B. (2018) · Australian & New Zealand Journal of Criminology · doi:10.1177/0004865817722393 · Cited by 14 · see citing works
Citizen Security: Conceptual Framework and Empirical Evidence
Abizanda, B., Hoffman, J. S., Marmolejo, L., & Duryea, S. (2012) · doi:10.18235/0006935 · Cited by 14 · see citing works
Algorithmic Decision Making in the Presence of Unmeasured ConfoundingPreprint
Jung, J., Shroff, R., Feller, A., & Goel, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.01868 · Cited by 14 · see citing works
Gill, C. E. (2010) · Scholarly Commons (University of Pennsylvania) · Cited by 13 · see citing works
Marciniak, D. (2022) · Policing & Society · doi:10.1080/10439463.2022.2144305 · Cited by 16 · see citing works
Berk, R. A. (2009) · Criminology & Public Policy · doi:10.1111/j.1745-9133.2009.00597.x · Cited by 12 · see citing works
Self-citationDevelopment of Aggression Detection Technique in Social Media
Zaib, S., Asif, M., & Arooj, M. (2019) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2019.05.05 · Cited by 11 · see citing works
Sherman, L. W. (2012) · doi:10.18235/0009044 · Cited by 11 · see citing works
Self-citationA predictive model for serious police misconduct by variation of the theory of planned behaviour
Cubitt, T. I. C., Wooden, K., Kruger, E., & Kennedy, M. (2020) · Journal of Forensic Practice · doi:10.1108/jfp-08-2020-0033 · Cited by 10 · see citing works
Machine Learning Can Predict Shooting Victimization Well Enough to Help Prevent It
Heller, S., Jakubowski, B., Jelveh, Z., & Kapustin, M. (2022) · National Bureau of Economic Research · doi:10.3386/w30170 · Cited by 9 · see citing works
Predicting and Preventing Gun Violence: An Experimental Evaluation of READI ChicagoPreprint
Bhatt, M., Heller, S., Kapustin, M., Bertrand, M., & Blattman, C. (2023) · doi:10.31235/osf.io/dks29 · Cited by 10 · see citing works
Using machine learning to forecast domestic homicide via police data and super learning
Verrey, J., Ariel, B., Harinam, V., & Dillon, L. C. (2023) · Scientific Reports · doi:10.1038/s41598-023-50274-2 · Cited by 9 · see citing works
Studying Parole in the “Spotlight”: Lessons from a Large American Jurisdiction
Vîlcică, E. R. (2015) · European Journal on Criminal Policy and Research · doi:10.1007/s10610-015-9284-8 · Cited by 9 · see citing works
Machine learning for policing: a case study on arrests in Chile
Wout, E. V. ’., Pieringer, C., Irribarra, D. T., Asahi, K., & Larroulet, P. (2020) · Policing & Society · doi:10.1080/10439463.2020.1779270 · Cited by 9 · see citing works
Robette, N. (2022) · Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique · doi:10.1177/07591063221128325 · Cited by 9 · see citing works
- Authors
- Berk, R., Sherman, L., Barnes, G., Kurtz, E., & Ahlman, L.
- Affiliations
- University of Pennsylvania (US)
- University of Cambridge (GB)
- First Judicial District of Pennsylvania , Philadelphia , USA
- Corresponding author
- Berk, Richard
- Venue
- Journal of the Royal Statistical Society Series A: Statistics in Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-07-24
- Volume
- 172
- Issue
- 1
- Pages
- 191-211
- ISSN
- 0964-1998 (ISSN-L)
- 1467-985X
- OpenAlex
- W2038763866
- Cited by
- 178
- References
- 51
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 6 · 2023: 10 · 2022: 13 · 2021: 6 · 2020: 12 · 2019: 14 · 2018: 13 · 2017: 12 · 2016: 16 · 2015: 9 · 2014: 7 · 2013: 15 · 2012: 16
- FWCI
- 24.22
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
- Funders
- National Science Foundation (SES-0437169)
- Topics
- Social Sciences › Sociology and Political Science › Criminal Justice and Corrections Analysis
- Social Sciences › Psychology › Clinical Psychology › Psychopathy, Forensic Psychiatry, Sexual Offending
- Social Sciences › Law › Criminal Justice and Penology
- Keywords
- probationers, parolees, recidivism, statistical learning, community supervision, sentencing guidelines, resource allocation, Philadelphia
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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