Jun Wu

Bio: Jun Wu is an academic researcher from Angelo State University. The author has an hindex of 1, co-authored 2 publications receiving 5 citations.

Exploring Violent Crime Reporting among School-Age Victims: Findings from NCVS SCS 2005-2015

Abstract: Most studies on the reporting of crimes to the police have focused on adult victims. Less is known, however, regarding crime reporting behavior among school-age victims. This paper presents finding...

The Relationship between Motivations for Joining Gangs and Violent Offending: A Preliminary Test on Self-Determination Theory

Abstract: Efforts to better understand violence have often compared gang members to non-gang members, finding, expectedly, that gang members engage in more violent behaviors. Often overlooked, however, is th...

Forecasting Identity Theft Victims: Analyzing Characteristics and Preventive Actions through Machine Learning Approaches

Abstract: Researchers in criminology and criminal justice have been making increasing use of the machine learning approach to investigate questions involving large amounts of digital data. We make use here o...

Public perceptions of police behavior during traffic stops: logistic regression and machine learning approaches compared

Abstract: Prior research on citizen perceptions of police has taken a wide-angle lens approach to the topic, with only a few studies investigating public perceptions of particular types of citizen–police encounters. In the current study, we make use of archival data on police traffic stops drawn from four waves of the BJS police–public contact surveys (PPCS) conducted in 2005, 2008, 2011, and again in 2015. In addition to employing conventional logistic regression, we make use of random forest classification to analyze survey data from a machine learning perspective. We use conventional logistic regression as a tool of explanation and random forest classification as a tool of prediction. We compare the findings generated by these two distinct analytical approaches. Substantive findings are quite similar for the explanatory and forecasting approaches. Driver’s belief that a traffic stop is legitimate is a major factor in how he or she evaluates police behavior in traffic stops, and whether the police use or threaten force during traffic stops may be the second most important factor. We draw out the implications of our work for our understanding of traffic stop dynamics, for the theory of procedural justice, for the theory of negativity bias, and for the enhanced use of machine learning in criminal justice.

Social Distance and Immediate Informal Responses to Violent Victimization

Abstract: There are a number of ways that victims of violence informally handle attacks as they unfold. Their responses range in severity from physical resistance, to talking it out with the offender, to running away, to cooperating. Why do victims respond in a more or less severe manner? Cooney (2009) suggests that social distance is part of the answer: the further the relational or cultural distance between offender and victim, the more severe the latter’s response. Using National Crime Victimization Survey data, we test hypotheses derived from this theory and find oppositional findings. Specifically, results indicate that closer social distance predicts more severe responses. We conclude by discussing the implications of this finding for future work, especially as relates to the study of self-protective behavior.