Write a three page critical Reflection on two articles The two articles are based on sentencing in the era of actuarial justice  The questions that need t

Write a three page critical Reflection on two articles The two articles are based on sentencing in the era of actuarial justice 

The questions that need to be answered within this critical reflection are: 

What is the statement or the claim the author(s) is/are making? What are the arguments for their claims? What are the supporting arguments? Do the supporting arguments make sense based on the data, the conceptual/ theoretical and the literature presented by the author(s)? What are the theoretical or literary connection of the current articles to other articles/ concepts discussed in lectures? Use APA style intext citation. 

These questions are to be answered throughout the critical reflection and not with subtitles. 

Tips:

Provide a short synopsis of each article, an overview of the main points, the arguments used to back up the main points, kind of like explaining the articles to someone who hasn’t read them, however this is not a summary of the articles 

Double spaced, 12pt font, Times new roman, APA style Punishment & Society

2017, Vol. 19(4) 463–481

! The Author(s) 2016

Reprints and permissions:

sagepub.co.uk/journalsPermissions.nav

DOI: 10.1177/1462474516666282

journals.sagepub.com/home/pun

Article

Socioeconomic
marginality in
sentencing: The built-in
bias in risk assessment
tools and the
reproduction of
social inequality

Gwen van Eijk
Leiden University, the Netherlands

Abstract

This article develops a sociological analysis and critique of including socioeconomic

factors such as education, employment, income and housing in risk assessment tools

that inform sentencing decisions. In widely used risk assessment tools such as the Level

of Service Inventory-Revised (LSI-R) (Canada, US), the Correctional Offender

Management Profiling for Alternative Sanctions (COMPAS) (US), the Offender

Assessment System (OASys) (UK) and the Recidive InschattingsSchalen (RISc) (the

Netherlands), socioeconomic marginality contributes to a higher risk score, which

increases the likelihood of a (longer) custodial sentence for underprivileged offenders

compared to their more privileged counterparts. While this has been problematized in

relation to gender and racial/ethnic bias, the problem of socioeconomic bias in itself has

received little attention. Given the already marginalized position of many justice

involved individuals and longstanding concerns about such disparities, and the adverse

effects of imprisonment on socioeconomic opportunities, it is essential to evaluate the

unintended social consequences of assessing socioeconomic marginality as ‘risk factor’.

Elaborating on earlier critiques, I conceptualize risk-based sentencing as a meaning-

making process through which (access to) resources and recognition are distributed

among offender populations. Through tracing in detail two cultural processes – stigma-

tization and rationalization – I analyse how risk assessment is likely to produce

Corresponding author:

Gwen van Eijk, Institute for Criminal Law and Criminology, Leiden Law School, Leiden University, P.O. Box

9520, 2300 RA, Leiden, the Netherlands.

Email: g.van.eijk@law.leidenuniv.nl

https://uk.sagepub.com/en-gb/journals-permissions

https://doi.org/10.1177/1462474516666282

journals.sagepub.com/home/pun

http://crossmark.crossref.org/dialog/?doi=10.1177%2F1462474516666282&domain=pdf&date_stamp=2016-09-05

sentencing disparities as well as to reproduce, and possibly exacerbate, social inequal-

ities more generally.

Keywords

disparity, risk assessment, sentencing, social inequality, socioeconomic marginality

Introduction

The use of actuarial risk assessment tools in sentencing decisions has become
common practice in various jurisdictions such as several states in the US,
Canada, the Netherlands and the UK (e.g. Fitzgibbon, 2008; Hannah-Moffat,
2013; Harcourt, 2007; Monahan and Skeem, 2015; Starr, 2015; van Wingerden
et al., 2016). Criminologists and legal scholars have criticized risk assessment
tools for their potential bias against racial/ethnic minorities and women, which
could result in sentencing disparities (e.g. Chenane et al., 2015; Hannah-Moffat,
2009, 2016; Hannah-Moffat and Maurutto, 2010; Harcourt, 2007; Holtfreter and
Cupp, 2007; Monahan, 2006; Petersilia and Turner, 1987; Skeem and Lowenkamp,
2016; Skeem et al., 2016; Smykla, 1986; Tonry, 1987, 2014; Whiteacre, 2006). While
many scholars have pointed to the role of assessing socioeconomic factors in pro-
ducing ethnic/racial and gender bias, relatively little attention has been given to the
problem of socioeconomic bias in itself (but see Goddard and Myers, 2016; Starr,
2014, 2015; Tonry, 2014; more below). Widely used risk assessment tools such as
the Level of Service Inventory-Revised (LSI-R) (used in Canada and the US), the
Correctional Offender Management Profiling for Alternative Sanctions
(COMPAS) (US), the Offender Assessment System (OASys) (UK) and the
Recidive InschattingsSchalen (RISc) (the Netherlands), which are applied to general
offender populations, include socioeconomic factors such as education, employ-
ment, financial situation and accommodation. This is problematic because, put
simply, socioeconomic marginality contributes to a higher risk score, which in
turn could translate into more severe sentences for these individuals (e.g. incarcer-
ation instead of a community sanction, or a longer sentence) compared to their
more privileged counterparts. Early critics of risk assessment have pointed out
that socioeconomic factors such as employment and education are ‘class-based
variables that, in effect, discriminate against the poor’ (Smykla, 1986: 130–131)
and that such factors ‘should be forbidden’ in sentencing criteria (Tonry, 1987:
408). More recently, Starr (2014, 2015) has offered an elaborate critique of ‘pun-
ishing based on poverty’ and the ‘scientific rationalization of discrimination’, while
Goddard and Myers (2016) have described risk-based assessment as ‘evidence-
based oppression’ of marginalized youth.

This largely conceptual article builds and elaborates on these critiques in order
to develop a sociological analysis that focuses more explicitly on how including
socioeconomic marginality as a risk factor in sentencing decisions reproduces – and

464 Punishment & Society 19(4)

possible exacerbates – disparities in sentencing as well as social inequality more
generally. Using the sociological concept of ‘cultural process’ (Lamont et al., 2014),
I conceptualize sentencing based on risk assessment as a process through which
resources and recognition are distributed, based on meaning-making through iden-
tification and rationalization. In the next section I briefly discuss the role of socio-
economic factors in risk assessment tools. I then introduce the concept of ‘cultural
process’ and examine how risk-based sentencing is likely to reinforce disparities
and inequalities as it works to distribute opportunities and resources differently
among convicted individuals based on their socioeconomic status. The article ends
with outlining several directions for further research and debate.

The practice and problem of including socioeconomic
marginality in risk assessment tools

Including socioeconomic factors in assessment tools is by no means a recent devel-
opment. For example, the first American risk assessment tool developed by Burgess
in the 1920s included ‘work record’ (for an overview of early risk assessment tools,
see Oleson, 2011). So-called second-generation tools such as the Violence Risk
Appraisal Guide (VRAG) and Static-99, typically only include static factors –
criminal history and certain demographic characteristics such as age – that may
change over the life course but cannot be targeted through intervention (for an
overview, see Hamilton, 2015). It is with the development of third-generation ‘risk/
needs’ assessment tools that aim to address ‘dynamic risk factors’ or ‘criminogenic
needs’ that socioeconomic factors were reintroduced (Andrews and Bonta, 2010).
The most well-known example probably is the LSI-R, developed in the 1980s in
Canada (Andrews and Bonta, 2010; for a detailed history, see Maurutto and
Hannah-Moffat, 2006). The LSI-R is also the most commonly used tool in
American jurisdictions (Hamilton, 2015; Harcourt, 2007). The tool is based on
Andrews and Bonta’s (2010) ‘psychology of criminal conduct’ theory and assesses
the ‘Central Eight’ domains that are related to reoffending, among which is ‘social
achievement’ (initially based on Gendreau et al., 1996). The tool consists of 54
items of which 10 items that measure education and employment (e.g. ‘currently
employed’, ‘never employed for a full year’, ‘ever fired’, ‘less than grade 10/12’),
two items that measure financial situation (‘financial problems’, ‘reliance on social
assistance’) and three items that measure accommodation (e.g. ‘unsatisfactory
housing’, ‘residential stability’) (Caudy et al., 2013; Hamilton, 2015).

Comparable risk/needs assessment tools are the OASys which is used in the UK
(Robinson, 2003), the RISc (translates as Recidivism Assessment Scales) in the
Netherlands (van Wingerden et al., 2016) and the COMPAS in several American
states (Hamilton, 2015; New York State (NYS), 2015). All three tools inform
treatment as well as sentencing and parole decisions and include items that measure
education/employment, financial situation and accommodation (Fitzgibbon, 2008;
NYS, 2015; van Wingerden et al., 2016). For example, the RISc consists of 62 items
in total, divided among 12 scales (3–8 items per scale) such as ‘accommodation’

van Eijk 465

(4 items), ‘education, work and training’ (7 items) and ‘financial management and
income’ (4 items); specific items include ‘problematic employment history’, ‘unem-
ployed/unable to work’, ‘work experience’, ‘lack of work skills’, ‘no education/
diploma’, ‘depends on others for income’, ‘financial problems’, ‘debts’, ‘history
of homelessness’, and ‘no suitable/permanent accommodation’ (van Wingerden
et al., 2016).

Socioeconomic factors can make up 10 to 25 per cent of the total number of
items assessed. For example, socioeconomic factors are assessed in 15 of 62 items in
total in the RISc, 15 of 54 items in the LSI-R; and add up to 10 of 100 points in the
OASys Violence Predictor (OVP), a variation of OASys for violent offenders
(Howard and Dixon, 2013). Their exact contribution to the overall risk score
may differ when items are weighted, which is not always transparent as sometimes
algorithms are proprietary (e.g. COMPAS). Furthermore, some of the other items,
such as those assessing leisure activities, seem correlated with socioeconomic status
(see Goddard and Myers, 2016). But even if socioeconomic factors would contrib-
ute little to the overall risk score, they could and do make the difference between
assessing an individual as ‘low’ or ‘moderate’ risk, or ‘moderate’ or ‘high’ risk
(Starr, 2014, 2015). Thus, that socioeconomic factors are among many factors
assessed cannot be an argument to disregard potential social consequences.
Regardless of how other items are scored, underprivileged individuals are con-
fronted with a higher risk score and, consequently, are more likely to face a cus-
todial sentence or to face a longer custodial sentence, compared to their more
privileged counterparts.

A critical evaluation of socioeconomic factors in risk assessment tools is
essential given concerns among scholars as well as criminal justice actors about
socioeconomic disparities in sentencing (Holder, 2014; Reiman and Leighton, 2016;
van Wingerden et al., 2016; Western, 2006). As Starr notes, basing risk assessment
on socioeconomic factors means using ‘dry, technical language to obscure discrim-
ination that we would otherwise never accept’ (2015: 229). Furthermore, ‘if judges
or policymakers would be embarrassed to embrace ideas like ‘‘we should increase
people’s sentences for being poor’’ openly, then they should not do so covertly by
relying on a risk score that is substantially driven by such factors’ (Starr, 2015).
‘Covert’ here should not be read as meaning ‘biased interpretation’ of items that
turn out to disadvantage certain social categories. To the contrary, and unlike
racial/ethnic and gender biases, socioeconomic bias results from direct discrimin-
ation on the basis of socioeconomic marginality: it is a built-in bias. ‘Covert’ in
Starr’s remark rather refers to the fact that many of the people involved (defend-
ants, lawyers), let alone the general public, are not aware that risk assessment
instruments inform sentencing decisions, or they do not know which and how
individual characteristics are assessed (Starr, 2015). Nonetheless, it may well be,
given the widespread support for current risk assessment tools, also among judges
(e.g. Kopf, 2015; Wolff, 2008), that judges and policymakers would not be embar-
rassed at all to defend sentencing based on socioeconomic status, as long as it can
be maintained that doing so contributes to public safety.

466 Punishment & Society 19(4)

It could be argued that risk assessment intends to discriminate (in the sense of
‘differentiate’) based on socioeconomic status because socioeconomic factors have
predictive validity: research shows that problems related to employment, educa-
tional, finances and housing are criminogenic factors and, more specifically, that
they predict reoffending (a widely cited source is Gendreau et al., 1996; specific
evaluations of the predictive validity of socioeconomic factors include Andrews
and Bonta, 2010; Howard and Dixon, 2013; Johnson et al., 2011; Raynor et al.,
2000). If socioeconomic factors improve predictions of reoffending, it could
be argued that it is irresponsible, in terms of public safety, to not include such
factors in sentencing decisions. There is an argument to be made that the results of
validity studies are inconclusive (e.g. Austin et al., 2003; Brennan et al., 2009;
Caudy et al., 2013; Petersilia and Turner, 1987). But rather than debating the
predictive validity, I wish to draw attention to the consequences of assessing socio-
economic marginality as risk factor. Notwithstanding the fact that the evidence
indeed points towards predictive validity, an evaluation of the outcomes requires
that we take into account both intended (public safety, efficiency) and unintended
outcomes (disparities, social inequality). In the 1980s, the use of factors such as
education, vocational skills, employment record, income and residential stability
was generally seen as ‘inappropriate’ for including in sentencing decisions by
American courts and parole boards (Tonry, 1987). Both the Minnesota sentencing
guidelines (1980) and the Sentencing Reform Act (1984) stated that such factors
could not be used in sentencing decisions due to their ‘socially skewed impact’
(Tonry, 1987: 398). Similarly, for race and race-related items, it has been decided
that discrimination and racial disparities are not acceptable, and thus race and
race-related items have been removed from predictive instruments (Harcourt,
2007; Tonry, 2014). Evaluating the legitimacy of practices based on criteria such
as validity and evidence base thus is too limited, as it pushes aside moral questions
and brushes over social-political, economic and cultural conditions that shape
socioeconomic marginality (cf. Goddard and Myers, 2016; Hannah-Moffat,
2016; Silver, 2000). Rather, we need to evaluate risk assessment in its moral and
social context.

Furthermore, analysing the potential discriminatory effects of risk assessment
has broader relevance. First, research in various countries has pointed to socio-
economic and racial disparities in sentencing (e.g. Kutateladze et al., 2014; Millie
et al., 2007; Reiman and Leighton, 2016; van Wingerden et al., 2016) and risk
assessment based on socioeconomic factors could play a role in how such dispa-
rities emerge. Second, there is increasing academic interest, particularly related to
mass incarceration in the US, in understanding how punishment, especially impris-
onment, might reinforce socioeconomic inequality more generally, considering the
adverse effects of prison sentences on education, work and income (Pager, 2008;
Wakefield and Uggen, 2010; Western, 2006; Western and Pettit, 2010). Such
adverse effects may also exist in less unequal and less punitive societies, such as
the Netherlands (Ramakers et al., 2014). It is difficult to determine whether certain
sentencing practices merely reflect existing inequalities, or whether such practices in

van Eijk 467

themselves create or reinforce inequalities, because offender populations are dis-
proportionally often socioeconomically marginalized and disadvantaged (Loeffler,
2013; Ramakers et al., 2015; Wakefield and Uggen, 2010). One way to gain more
insight into the ways in which sentencing disparities emerge and in how sentencing
might in itself produce inequality, is to trace in more detail the processes through
which sentences are imposed and how this impacts opportunities and resources.
This article aims to do so by zooming in on the role of risk assessment.

It should be noted that risk/needs assessment tools may be used for multiple
purposes, ranging from informing sentencing and parole decisions to guiding deci-
sions on treatment and supervision (Hannah-Moffat, 2005; Monahan and Skeem,
2015). This article focuses on the role of risk assessment in sentencing decisions. It is
particularly in sentencing decisions that including socioeconomic factors is prob-
lematic – more so than in decisions about supervision and treatment (cf. Starr,
2014). In reality, however, the goals of sentencing and treatment cannot always be
separated, as judges may impose treatment conditions as part of a sentence. I
return to the intertwining of risk and needs assessment, and sentencing and treat-
ment later in this article. For analytical purposes I simplify the decision-making
process and focus on the potentially differentiating effect of a convicted individual’s
risk score in two types of decisions: (1) whether or not to impose a custodial
sentence (jail or prison), as opposed to diversion (a community sentence, treatment
or fine) – as is for example explicitly the goal in the state Virginia (Kleiman et al.,
2007) – and (2) determining the duration of a custodial sentence – including eligi-
bility for parole, which in effect is also a decision about the duration of imprison-
ment (Harcourt, 2007). In the following section I examine in more detail how risk
assessment could reinforce disparities and inequality.

Tracing the cultural processes that produce social inequality

Statistics on the incarcerated population in the US show that socioeconomic dispa-
rities have increased significantly between 1980 and 2010: ‘the spectacular growth in
the American penal system over the last three decades was concentrated in a small
segment of the population, among young minority men with very low levels of edu-
cation’ (Western and Pettit, 2010: 18). But also in countries with lower levels of
inequality and lower incarceration rates, the population that is under control of
the criminal justice system tends to be poorer, lower skilled and more often homeless,
compared to the general population. In the Netherlands, for instance, the labour
market position of convicted individuals prior to their imprisonment is already
weak: ‘Starting with a low educational attainment, their subsequent employment
career is characterized by long periods of unemployment, ‘‘off-the-books’’ employ-
ment, dismissals and job shifts’ (Ramakers et al., 2014: 65). Starr (2014, 2015) has –
in her elaborate critique on ‘evidence-based sentencing’ in the US – made the point
that including socioeconomic factors in risk assessment will increase sentencing
disparities rather than reduce them. Risk assessment classifies as ‘higher risk’ exactly
those social categories that are already overrepresented in the criminal justice

468 Punishment & Society 19(4)

system: the un- or low-skilled, the unemployed and the unhoused. When sentencing
decisions, especially decisions about whether or not to incarcerate someone, are
partly based on evaluations of socioeconomic status, as is done through risk assess-
ment, the link between sentencing and socioeconomic status is reinforced. Harcourt
(2007) has called this the ‘ratchet effect’: prediction instruments and profiling prac-
tices increase the disproportionality between the composition of the actual offender
population and the population that is in some way under control of the criminal
justice system. Harcourt stresses that it is important to recognize that this effect is
produced even if the relationship between socioeconomic marginality and reoffend-
ing is real (i.e. not an artefact of criminal justice policies and practices). Whether
‘evidence based’ or not, a greater focus on particular groups – more policing, more
prosecution, more imprisonment, longer sentences – means that their share in the
total population that is under control of the criminal justice system will grow, and
continue to grow, and that disparities will increase (Harcourt, 2007).

Moreover, when sentencing affects an individual’s opportunities in life, it also
shapes social inequality in societies more generally. Following Fraser (1995), we
can broadly define social inequality as the unequal distribution of (access to)
resources that matter for people’s quality of life and wellbeing, including material
and immaterial resources as well as recognition (cf. Lamont et al., 2014). Various
studies have demonstrated the adverse effects of (length of) imprisonment on edu-
cational achievement, employment opportunities, income and housing (e.g. Apel
and Sweeten, 2010; Pager, 2008; Ramakers et al., 2014; Western, 2006). In this way,
we can understand sentencing – and criminal-justice decisions more generally – as a
social process through which resources are distributed and, consequently, social
inequality is either reduced or reinforced. Even if adverse effects are largely selec-
tion effects (because the offender population is already underprivileged, see
Loeffler, 2013), it seems safe to say, based on research, that imprisonment makes
it difficult for individuals to maintain, let alone improve, the quality of their life and
wellbeing (e.g. Dirkzwager et al., 2014). The ‘best case’ scenario is thus that senten-
cing based on biased risk assessment merely reproduces inequality; in the worst
case it exacerbates inequality.

To better understand how this would work, I draw on the work of Lamont et al.
(2014) to conceptualize sentencing based on risk assessment as a decision process
that is grounded in two ‘cultural processes’: identification and rationalization.
Cultural processes link cognitive categorizations in people’s minds, on the one
hand, and the distribution of important resources and recognition, on the other.
Put differently, through ‘inter-subjective meaning-making’ people create shared
classification systems that ‘sort out’ people and actions (Lamont et al., 2014:
582). When classification systems inform actual decisions that impact people’s
life, the sorting out of people is translated into the (unequal) distribution of
resources among categories of people, thus shaping access to resources and hier-
archies of status and worth. It is in particular the state, Lamont et al. (2014: 585)
argue, that ‘wields immense power in shaping and legitimizing systems of categor-
ization’ through designing and executing laws and social programs. They further

van Eijk 469

stress that the production of inequality does not have to be intentional; it could be
an unintended consequence of other, intentional actions. Without making any
assumptions about punitive sentiments – either of governments or of the general
public, or both – towards socially marginalized groups, the analytical approach
proposed by Lamont and colleagues is to trace the processes through which
resources and recognition are distributed. In this way, we can shed new light on
the relation between sentencing, particularly imprisonment and social inequality.

Lamont et al. (2014) have theorized that ‘identification’ and ‘rationalization’ are
two broad types of cultural processes that are central in producing inequality.
Identification is ‘the process through which individuals and groups identify them-
selves, and are identified by others, as members of a larger collective’; stigmatiza-
tion, or the negative qualification of identities, is an example (Lamont et al., 2014:
587). Rationalization, famously described by Max Weber, broadly refers to ‘the
displacement of tradition and values as motivation for action by a means-end
orientation’, for example through standardization and evaluation (Lamont et al.,
2014: 591). Both processes are central in risk assessment based on socioeconomic
marginality (cf. Silver, 2000). Risk assessment evaluates socioeconomic marginality
negatively, first by the mere fact that it is included as a risk factor, second by
prescribing that a higher overall risk factor is interpreted to mean a greater
danger to the public and thus should translate into a higher sentence. Both
Harcourt (2007) and Starr (2014, 2015) have pointed to the symbolic message
that is associated with disproportionate sentencing based on certain offender char-
acteristics. Starr is concerned with treating poverty as a risk factor, which ‘is also
endorsing a message: that it considers certain groups of people dangerous because
of who they are, not what they have done’ (2015: 230). Profiling based on predic-
tion about who will (re)offend comes with a social cost, Harcourt (2007) argues,
because it tends to create unintended stigma that attaches to, in this case, the
unemployed, unskilled and unhoused. Incarceration or longer sentences for offen-
der categories who are assessed as higher risk because of their socioeconomic status
reinforces the negative identification of socioeconomic marginality. Through defin-
ing which social categories are worthy of beneficial policies and which categories
deserve punitive policies, policymakers allocate resources (Gans, 1995). Public
policies in turn send messages about which categories are deserving and what are
appropriate kinds of attitudes towards them (Schneider and Ingram, 1993).
Deserving citizens are supported, while undeserving citizens tend to be punished.
In sentencing decisions this means: socioeconomically ‘integrated’ or ‘productive’
offender categories – those who are skilled, employed or employable, and with a
stable residence – are more deserving of diversion away from imprisonment, so that
they can maintain their socioeconomic status. Socioeconomically marginal offender
categories, on the other hand, are undeserving of diversion and, consequentially, of
the opportunity to improve their socioeconomic status, as their options to develop
a mainstream way of life are hindered during and after imprisonment. In addition,
when risk assessment is legitimized based on the supposed benefits of diversion for
low-risk individuals, while the costs of non-diversion for high-risk individuals (i.e.

470 Punishment & Society 19(4)

incarceration and all its consequences) are ignored, the message is reinforced that
convicted individuals who are also underprivileged are less worthy of opportunities
to improve their life.

What is more, risk assessment reinforces the notion that socioeconomic margin-
ality is the sole responsibility of the individual (Goddard and Myers, 2016;
Hannah-Moffat, 2016). This is not surprising, given that current risk assessment
tools are grounded in a social-psychological theory of offending that looks for
explanations related to the individual rather than to society (Andrews and
Bonta, 2010). But it does more. Andrew and Bonta’s argument for including socio-
economic achievement in the LSI-R tool on the ground that it is an ‘achieved’
status, as opposed to ‘social class’ (family background) which would be an
‘ascribed status’, is consistent with the dominant cultural narrative in contempor-
ary Western societies that we live in ‘classless’ societies in which people shape their
own life course (e.g. Beck, 1992; Lamont, 2000; Savage, 2000; van Eijk, 2013).
However, research in Europe and the US shows that opportunity structures are
less open than we would expect in classless societies: people’s educational and
income level still depend on the socioeconomic status of their parents (Causa
and Johansson, 2009; Putnam, 2015). In addition, structural factors such
as rising inequality hamper intergenerational social mobility (Corak, 2013).
Socioeconomic opportunities more generally are also affected by welfare reforms,
economic crises and stagnating wages for low- and mid-level jobs, which have led
to a growing number of people, including those who are working, living in poverty.
By disregarding the social and historical context of the marginal positions of social
groups, risk assessment individualizes socioeconomic marginality and thus shifts all
responsibilities to individuals not only for the crime they have committed but also
for their socioeconomic status (Goddard and Myers, 2016; Hannah-Moffat, 2016;
see also Hannah-Moffat and Maurutto, 2010, on the individualization of gendered
and racial inequality). The individualization of class is in a specific way problematic
for individuals who come into contact with the criminal justice system repeatedly,
as social structures can enable as well as constrain possibilities for agency in desis-
tance processes (Farrall et al., 2010; …

Looking for this or a Similar Assignment? Click below to Place your Order