Online journals' impact on the citation patterns of medical faculty.

PURPOSE: The purpose was to determine the impact of online journals on the citation patterns of medical faculty. This study looked at whether researchers were more likely to limit the resources they consulted and cited to those journals available online rather than those only in print. SETTING: Faculty publications from the college of medicine at a large urban university were examined for this study. The faculty publications from a regional medical college of the same university were also examined in the study. The number of online journals available for faculty, staff, and students at this institution has increased from an initial core of 15 online journals in 1998 to over 11,000 online journals in 2004. METHODOLOGY: Searches by author affiliation were performed in the Web of Science to find all articles written by faculty members in the college of medicine at the selected institution. Searches were conducted for the following years: 1993, 1996, 1999, and 2002. Cited references from each faculty-authored article were recorded, and the corresponding cited journals were coded into four categories based on their availability at the institution in this study: print only, print and online, online only, and not owned. Results were analyzed using SPSS. RESULTS: The number of journals cited per year continued to increase from 1993 to 2002. The results did not indicate that researchers were more likely to cite online journals or were less likely to cite journals only in print. At the regional location where the number of print-only journals was minimal, use of the print-only journals did decrease in 2002, although not significantly. CONCLUSION/DISCUSSION: It is possible that electronic access to information (i.e., online databases) has had a positive impact on the number of articles faculty will cite. Results of this study suggest, at this point, that faculty are still accessing the print-only collection, at least for research purposes, and are therefore not sacrificing quality for convenience.     

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.