Abstract
This paper adopts a conventionalist approach to shed light on the measurement and reification problems that underlie the quantification of desistance from crime in the scientific literature. Analysis of 100 papers spanning three decades indicates that approaches based on theoretical classification have recently lost ground in favor of more sophisticated techniques aimed at empirically identifying subgroups. These techniques convey the impression of objectiveness among statistics users and consumers and, as a result, the classification “desisters” and “persisters” are increasingly reified. Findings suggest that the quantification of desistance is intimately linked to the maintenance of a classification system that constitutes delinquency as a stable category and contributes to “making” up new kinds of people over which institutions can legitimately intervene.
Highlights
MethodsWe extracted the following data from the included texts: discipline of first author, theoretical definition of desistance, analytic strategy, and use of labels
This paper adopts a conventionalist approach to shed light on the problems of measurement and reification that underlie the quantification of desistance from crime in the scientific literature
The line connecting the three timeframes suggests that desistance research has moved from a paradigm where desistance is defined as a termination, analysed using theoretical classifications, and often studied by psychiatrists, to a paradigm where desistance is defined as a process and primarily studied by criminologists
Summary
We extracted the following data from the included texts: discipline of first author, theoretical definition of desistance, analytic strategy, and use of labels. We provided effect sizes (Cramer’s V) and conducted joint correspondence analysis on the indicator matrix, a geometric, descriptive method that highlights relations between categorical variables (Greenacre, 2017) It provides a visual representation of the characteristics of eligible papers in bi-dimensional space. The calculation of variance explained is improved and bias in the visual representation reduced (Greenacre, 2017) For these purposes, we categorized year of publications (< 1999, 2000-2009, 2010-2019) and excluded papers from ‘Other’ disciplines or mobilizing ‘Other’ analytic strategies categories
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