Automated Classification to Improve the Efficiency of Weeding Library Collections

Previous studies have shown that weeding a library collection benefits patrons and increases circulation rates. However, the time required to review the collection and make weeding decisions presents a formidable obstacle. This study empirically evaluated methods for automatically classifying weeding candidates. A data set containing 80,346 items from a large-scale weeding project running from 2011 to 2014 at Wesleyan University was used to train six machine learning classifiers to predict a weeding decision of either ‘Keep’ or ‘Weed’ for each candidate. The study found statistically significant agreement (p?=?0.001) between classifier predictions and librarian judgments for all classifier types. The naive Bayes and linear support vector machine classifiers had the highest recall (fraction of items weeded by librarians that were identified by the algorithm), while the k-nearest-neighbor classifier had the highest precision (fraction of recommended candidates that librarians had chosen to weed). The variables found to be most relevant were: librarian and faculty votes for retention, item age, and the presence of copies in other libraries.     

Mandatory Student Fees and the Academic Library

As academic libraries' budgets remain flat while their expenditures rise, library leaders must seek out alternative sources of funding. One option is a mandatory student fee dedicated to the library. This article presents the results of a survey of 152 deans and directors of public R1 and R2 research universities that were asked whether their libraries benefit from mandatory student fees. While a library-specific fee is unusual, nearly half of responding libraries are eligible to receive some portion of another mandatory student fee. Study results can serve as evidence for libraries preparing to make a case for the significant, positive impact possible through even a modest library fee.     

Russian Science Citation Index: Areas for Development Identified by Active Users

The current state of the Russian Science Citation Index is analyzed. Several areas for its further development identified by long-standing active users, such as data searching, refinement, and storage; current user alerts; data input with the Science Index license; patent information; and the application programming interface (API) are investigated.     

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.     

Narrative Persuasion in a New Media Environment: The Impact of Binge-Watching and Second-Screening

The study explored the role of second-screen use and binge-watching in moderating entertainment education (EE) effects. A pretest/posttest experiment of 273 viewers of East Los High measured the effects of exposure to three subplots, concerning alcohol abuse, abortion, and immigration. The effect of identification with characters on change in attitudes was significantly moderated by second-screen use and binge-watching. In particular, second-screening tended to enhance the positive impact of identification; whereas, among respondents who binge-watched the show, the effects of identification were weaker.     

Reasons for Student Engagement in Extra-Class Communication

This study extends instructional communication research on extra-class communication (ECC) by presenting an integrative analysis comparing the explanatory utility of student- and instructor-oriented reasons for student engagement in frequent ECC. Results from 495 college students suggested that student-oriented reasons, specifically the relational and functional student motives, were the only significant, positive predictors of students’ perceptions of frequent ECC engagement. Other student motives (excuse making, participation, sycophancy) and perceptions of instructor approachability did not significantly predict perceived frequency of ECC engagement.     

It’s More Than Self-Presentation: MUM Effects Can Reflect Private Discomfort and Concern for the Recipient

Is the reluctance to share bad news (i.e., the MUM effect) motivated more by a public display or private concern, and does it benefit mainly the messenger or the recipient? An experiment (N = 309) that crossed good/bad news with three communication channels (face-to-face, text messaging, e-mail) revealed that messenger reluctance was greatest under conditions of bad news and did not vary based on the channel through which the recipient contacted the messenger. In contrast with earlier work, this MUM effect was more consistent with a private fear of distressing the recipient. Theoretical implications and limitations 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.     

Technological research in the EU is less efficient than the world average. EU research policy risks Europeans’ future

We have studied the efficiency of research in the EU by a percentile-based citation approach that analyzes the distribution of country papers among the world papers. Going up in the citation scale, the frequency of papers from efficient countries increases while the frequency from inefficient countries decreases. In the percentile-based approach, this trend, which is uniform at any citation level, is measured by the e_p index that equals the P_{top 1%}/P_{top 10%} ratio. By using the e_p index we demonstrate that EU research on fast-evolving technological topics is less efficient than the world average and that the EU is far from being able to compete with the most advanced countries. The e_p index also shows that the USA is well ahead of the EU in both fast- and slow-evolving technologies, which suggests that the advantage of the USA over the EU in innovation is due to low research efficiency in the EU. In accord with some previous studies, our results show that the European Commission’s ongoing claims about the excellence of EU research are based on a wrong diagnosis. The EU must focus its research policy on the improvement of its inefficient research. Otherwise, the future of Europeans is at risk.