Sentencing Using Statistical Treatment Rules: What We Don’t Know Can Hurt Us
The existing literature seriously misinterprets the available evidence on the predictability of high rate criminal offending and thus the potential value of statistical treatment rules that impose stiffer punishments on offenders with higher predicted risk of recidivism. The misinterpretation result...
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| Tipo de documento: | Electrónico Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2007
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| En: |
Journal of quantitative criminology
Año: 2007, Volumen: 23, Número: 4, Páginas: 377-387 |
| Acceso en línea: |
Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
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| Verificar disponibilidad: | HBZ Gateway |
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| Sumario: | The existing literature seriously misinterprets the available evidence on the predictability of high rate criminal offending and thus the potential value of statistical treatment rules that impose stiffer punishments on offenders with higher predicted risk of recidivism. The misinterpretation results from the failure to take account of the fact that the data used in existing risk assessment exercises come from environments characterized by informal (and sometimes formal) attempts by judges and other actors to base penal treatments on expected recidivism. Findings of little or no predictive power for baseline covariates may simply indicate the efficient use of the available information. We lay out the problem in detail, provide examples from several literatures and then consider general solutions to the problem. |
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| ISSN: | 1573-7799 |
| DOI: | 10.1007/s10940-007-9035-1 |