Technological capital: an alternative to the digital divide

This paper identifies individuals’ accumulated history with Information and Communication Technologies (ICTs) as a replacement for the contested and outdated term ‘digital divide.’ We propose that an individual’s ability to benefit from their technological history consists of four factors: awareness, knowledge, access, and technological capacity of the user’s social collective. Together, these factors make up the individual’s level or amount of Technological Capital. The development of this construct was based on empirical research conducted in Soweto, South Africa, and through an analysis of the literature on assessing individuals’ accumulated histories. The concept of technological capital has the potential for application in fields in which ICTs are used for communication, capacity building, and identifying barriers to adoption of new technologies. The use of the concept may have implications for policy decisions, resource allocation, and future research into differences among individuals’ and communities’ ability to benefit from the introduction of new technologies.     

Historical case study: A research strategy for diachronic analysis

History and case study are two diachronic research strategies commonly used to study political discourse as it relates to public libraries. Though suitable for some studies, these strategies are inexhaustive. A new, blended research strategy is therefore needed to accomplish what history and case study alone cannot. This hybrid strategy, historical case study, must analyze cases from the distant past to the present, using eclectic data sources, in order to produce both idiographic and nomothetic knowledge. To develop historical case study, the universal components of diachronic analysis are first identified. A general research design for historical case study is then introduced. Finally, historical case study is tested in an actual research project. Findings from this project reveal that historical case study is a successful diachronic research strategy. Historical case study is a new and valuable research design suitable for addressing questions related to change, continuity, development, and evolution.     

A Model for Quantitatively Analyzing Representations of Social Class in Screen Media

Content analyses of prime-time television provide a picture of predominant cultural stereotypes and social trends. The present research is a quantitative content analysis of social class in popular broadcast and cable television in the United States. This research advances scholarship by systematically analyzing character class in addition to more commonly examined variables such as race and gender. A model for the operationalization of class for quantitative content analyses of mediated texts is offered. This proof of concept study demonstrates the viability of studying media representations of class quantitatively to systematically document patterns of portrayal.     

How Persuasive Are Health Advertisements in First-Person Shooter Games? Exploring Knowledge-Activation and Thought-Disruption Mechanisms

This study examined whether persuasive health messages embedded in shooter games have broad or targeted effects on players’ willingness to engage in risk behaviors (N = 145). Participants presented with in-game health messages discouraging alcohol-impaired driving of motor vehicles showed reduced willingness to drink alcohol and to operate both motor and nonmotor vehicles, compared to those in a no-message gaming control condition. There were no spillover effects on willingness to smoke cigarettes or marijuana, thus implying targeted persuasive effects. In addition, players experiencing high instead of low cognitive load showed decreased postexperiment willingness to drink and operate nonmotor vehicles, thus suggesting that playing a game under high cognitive load can influence players’ attitudes. The findings replicate previous research and further expand on knowledge-activation and thought-disruption mechanisms underlying the persuasiveness of health messages.     

Preparing for Practice: Strengthening Third-Year Medical Students’ Awareness of Point-of-Care Resources

This column describes the development and implementation of a responsive library course designed to train third-year medical students as they begin their clerkships. The course design consisted of a brief face-to-face introduction during third-year orientation and an online course developed in Canvas, an online learning management system. The objective of the course was to not only introduce students to the resources but also to teach them how to effectively use those resources at the point-of-care. Students evaluated the course to assess the overall effectiveness of the instruction. Course development and content, feedback provided by students, as well as suggestions for improvement are discussed.     

Retrieving and classifying instances of source code plagiarism

Automatic detection of source code plagiarism is an important research field for both the commercial software industry and within the research community. Existing methods of plagiarism detection primarily involve exhaustive pairwise document comparison, which does not scale well for large software collections. To achieve scalability, we approach the problem from an information retrieval (IR) perspective. We retrieve a ranked list of candidate documents in response to a pseudo-query representation constructed from each source code document in the collection. The challenge in source code document retrieval is that the standard bag-of-words (BoW) representation model for such documents is likely to result in many false positives being retrieved, because of the use of identical programming language specific constructs and keywords. To address this problem, we make use of an abstract syntax tree (AST) representation of the source code documents. While the IR approach is efficient, it is essentially unsupervised in nature. To further improve its effectiveness, we apply a supervised classifier (pre-trained with features extracted from sample plagiarized source code pairs) on the top ranked retrieved documents. We report experiments on the SOCO-2014 dataset comprising 12K Java source files with almost 1M lines of code. Our experiments confirm that the AST based approach produces significantly better retrieval effectiveness than a standard BoW representation, i.e., the AST based approach is able to identify a higher number of plagiarized source code documents at top ranks in response to a query source code document. The supervised classifier, trained on features extracted from sample plagiarized source code pairs, is shown to effectively filter and thus further improve the ranked list of retrieved candidate plagiarized documents.