Jonathan Grossman and Ami Pedahzur
Since 2001, unprecedented resources have been invested in research into global terrorism, resulting in a dramatic rise in the number of academic publications on the topic. Works by scholars from predominantly quantitative disciplines predominate in this literature, and the unfolding development of data science and big data research has accentuated the trend. Many researchers in global terrorism created event databases, in which every row represents a distinct terrorist attack and every column a variable (e.g., the date and location of the attack, the number of casualties, etc.). Such event data are usually extracted from news sources and undergo a process of coding—the translation of unstructured text into numerical or categorical values. Some researchers collect and code their data manually; others use an automated script, or combine the efforts of humans and software. Other researchers who use event data do not collect and process their data at all; rather, they analyze other scholars’ databases. Academics and practitioners have relied on such databases for the cross-regional study of terrorism, analyzing their data statistically in an attempt to identify trends, build theories, predict future incidents, and formulate policies.
Unfortunately, event data on terrorism often suffer from substantial issues of accuracy and reproducibility. A comparison between the data on suicide terrorism in Israel and the occupied Palestinian territories in two of the most prominent databases in the field and an independent database of confirmed events reveals the magnitude of these problems. Among the most common pitfalls for event data are replication problems (the sources that the databases cite, if there are any at all, cannot be retrieved), selection bias (events that should have been included in the database are not in it), description bias (the details of events in the database are incorrect), and coding problems (for example, duplicate events). Some of these problems originate in the press sources that are used to create the databases, usually English-language newspaper articles, and others are attributable to deficient data-gathering and/or coding practices on the part of database creators and coders. In many cases, these researchers do not understand the local contexts, languages, histories, and cultures of the regions they study. Further, many coders are not trained in qualitative methods and are thus incapable of critically reading and accurately coding their unstructured sources. Overcoming these challenges will require a change of attitude: truly accurate and impactful cross-regional data on terrorism can only be achieved through collaboration across projects, disciplines, and fields of expertise. The creators of event databases are encouraged to adopt the high standards of transparency, replicability, data-sharing, and version control that are prevalent in the STEM sciences and among software developers. More than anything, they need to acknowledge that without good and rigorous qualitative work during the stage of data collection, there can be no good quantitative work during the stage of data analysis.
James M. Binnall and Maryanne Alderson
Reentry is the process of ending a period of incarceration, leaving jail or prison, and returning to society. Not to be confused with reintegration or recidivism, reentry is not a measure of success or failure. Instead, reentry is a journey, and no two reentries are analogous. The reentry process is individualized and highly dependent on a number of factors including a reentering individual’s sentence structure, incarceration experience, and postrelease resources. Depending on these factors, a reentering individual may find his or her return to the free world a relatively smooth transition or a task riddled with seemingly insurmountable obstacles. To navigate such obstacles, most reentering individuals need assistance. Traditionally, reentry assistance was provided by the state, through correctional programming in prison or by parole authorities tasked with monitoring a reentering individual postrelease. In recent years, nonprofit and faith-based organizations have increasingly been a part of innovative reentry initiatives. There has also been a recent expansion of Internet-based reentry resources, such as reentry.net and exoffenders.net, which allow those experiencing reentry to obtain reentry resources online.
Reentry initiatives typically take two forms: deficit-based and strengths-based. Deficit-based reentry models use actuarial assessments to identify a reentering individual’s criminogenic risks and needs. In theory, deficit-based models then address those risks and needs through measured, tailored responses. Critics of deficit-based models argue that by focusing only on risks and needs, such approaches overlook reentering individuals’ talents and skills. Acknowledging these criticisms, many reentry initiatives have shifted away from the traditional deficits-centered model of reentry and toward a strengths-based approach. Rather than focusing on the risks and needs of a reentering individual, strengths-based approaches highlight the attributes of reentering individuals and draw on the experiences of former offenders who have successfully navigated their own reentry and best understand the pitfalls of the process. Recent, albeit limited empirical and experiential evidence supports the strength-based approach to reentry, suggesting that the concerns and insights of those who have been directly impacted by the criminal justice system make the transition from incarceration to freedom a smoother one.
Christian L. Bolden and Reneé Lamphere
Social networks in gangs refers to both a theoretical and methodological framework. Research within this perspective challenges the idea of gangs as organized hierarchies, suggesting instead that gangs are semi-structured or loosely knit networks and that actions are more accurately related to network subgroupings than to gangs as a whole. The situated location of individuals within a network creates social capital and the fluidity for members to move beyond the boundaries of the group, cooperating and positively interacting with members of rival gangs. Before the millennium, the use of social network analysis as a method to study gangs was rare, but it has since increased in popularity, becoming a regular part of the gang research canon. Gang networks can be studied at the group level and the individual level and can be used for intervention strategies. The concept of gangs as social networks is sometimes confused with social networking sites or social media, which encompasses its own rich and evolving array of gang research. Gang members use social networking sites for instrumental, expressive, and consumer purposes. While the use of network media allows for gang cultural dissemination, it simultaneously allows law enforcement to track gang activity.
Finn-Aage Esbensen and L. Thomas Winfree
The socio-demographic characteristics of gang-involved youth are a focal concern of contemporary gang researchers; policy analysts; politicians; and, in many cases, the general public. A broad overview of gang member characteristics is a critical and natural precursor for any policy response to gangs, a task that has historically included widely used socio-demographic characteristics (e.g., race or ethnicity, age, urban or rural residence, gender, and sex) and various forms of illegal and illicit behavior. Similar lists of individual and collective characteristics such as these have shaped public policy responses to youth gangs in the United States, Western Europe, and indeed around the globe. Furthermore, given the attention paid to “illegal” migration trends at the end of the 21st century’s second decade, policymakers, law enforcement officials, and others often tie immigrant status to gang membership, including immigrants’ alleged involvement in violent forms of delinquency. The following image of street gang members emerges: (a) gangs include girls as well as boys; (b) the sex composition of the gang affects the level of delinquency of gang members; (c) gang members reflect the racial or ethnic composition of the community in which they exist; (d) gang members are not disproportionately members of immigrant groups; (e) youth age in and out of gangs during early- to mid-adolescence; and (f) while in the gang, youth commit significantly more crime than their non-gang peers.