The Crime-Specific Model: A New Criminal Justice Perspective

Abstract

The objectives of the criminal justice system are ambiguous and often conflicting. Attempts to improve law enforcement, on the one hand, and the administration of justice, on the other, result in an impasse which prohibits optimization of the operation of the system.

Therefore, quantitative analysis is almost always based on one of two conceptual models: the process model or the component model. This paper introduces a third conceptualization which is described as the crime-specific model.

The crime-specific perspective approaches criminal justice prob lems by considering subproblems categorized according to type of offense. This framework should facilitate planning and action in criminal justice. More pragmatic goals about specific crimes could be set and the evaluation of the achievement of these goals would be more direct. New concepts of organization throughout the system would be produced and the management of day-to-day operations would be streamlined.

In order for the crime-specific perspective to be used, two con ditions must exist. First, the circumstances surrounding the com mission of the offense and the characteristics of the offender must be measurably different for each offense category. Second, these differences must remain stable over time and across juridictions. This paper explores the extent to which events and offenders meet these constraints.

¹S. E. Kolodney, P. L. Patterson, D. Daetz, and R. L. Marx, A Study of the Characteristics and Recidivism Experience of California Prisoners, Volume I: Summary Report, (San Jose, California: Public Systems Incorpo rated, February, 1970).

²Newly committed to prison excludes pa role violators returned, but permits offend ers to have had prior prison commitments. ³The number of new felony admissions during the years 1960-1968 for whom full data were available.

⁴ F _α = 4.49 and F _n = 8.53 correspond to 95 and 99 percent confidence points on the F-distribution. These are common values for statistical tests.