Predictors of Individuals’ Behavioral Characteristics of Routine Activities Theory: Analysis of a Synthesis Model of Socio-Economic Status, Victimization, and Fear of Crime

Researchers have studied victimization, fear of crime, and individuals’ behavioral characteristics to investigate the origin of crime and victimization, such as the routine activity theory. However, little research has examined how the behavioral characteristics were formed in theory. Although the elements of socioeconomic status, victimization experience, and fear of crime are believed to cause differences in human behaviors, the current study attempts to examine which predictors construct behavior characteristics like the routine activity theory, including target suitability and guardianship. Using the most recent, nationally collected official crime victimization data from South Korea (Korean Crime Victimization Survey, 2014), the study analyzed the variables with statistical models. The results suggest the following:(1) an individual’s socioeconomic status – such as gender, age, and education level – rather than victimization experience or fear of crime, are significant predictors of target suitability;(2) higher levels of fear of crime predict higher levels of guardianship; and (3) the victimization experience did not predict either target suitability or guardianship

The Research-Teaching Nexus: Not merely an enduring myth

For more than a century, whether or not the research-teaching nexus exists has remained an intensely debated issue in the global academy at both the conceptual and empirical levels. Situating teaching styles within the context of teaching, conceptualizing research agendas as a dimension of research, and using academic self-efficacy as a mediator, the present study empirically investigated the research-teaching nexus. Participants were 256 academics in science, technology, engineering, and mathematics (STEM) fields from all of the eight institutions funded by the University Grants Committee in Hong Kong. In the context of participating in the “Academic Profession in the Knowledge-based Society” (APIKS) international survey between late 2017 and early 2018, the participants responded to a short version of the Multi-Dimensional Research Agendas Inventory, a short version of the Research-Teaching Efficacy Inventory, and two scales from the Thinking Styles in Teaching Inventory. Results showed that academics’ research agendas statistically predicted their teaching styles – after age, gender, academic rank, and institutional ranking were considered. Furthermore,academic self-efficacy, especially research efficacy, provided a pathway from research agendas to one of the two teaching styles examined. Limitations and theoretical contributions of the research are discussed; and practical implications of the research findings are proposed for academics in STEM fields and for university senior managers.This research was funded by the General Research Fund (Grant number: 17604015) as administered by the University Grants Council of the Hong Kong Special Administrative Region, the People’s Republic of China

Inferring Social Media Users’ Demographics from Profile Pictures: A Face++ Analysis on Twitter Users

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80% are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20%. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collection

Who are your users?:Comparing media professionals’ preconception of users to data-driven personas

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