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.     

Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics

The purpose of this study is to find a theoretically grounded, practically applicable and useful granularity level of an algorithmically constructed publication-level classification of research publications (ACPLC). The level addressed is the level of research topics. The methodology we propose uses synthesis papers and their reference articles to construct a baseline classification. A dataset of about 31 million publications, and their mutual citations relations, is used to obtain several ACPLCs of different granularity. Each ACPLC is compared to the baseline classification and the best performing ACPLC is identified. The results of two case studies show that the topics of the cases are closely associated with different classes of the identified ACPLC, and that these classes tend to treat only one topic. Further, the class size variation is moderate, and only a small proportion of the publications belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable to determine the topic granularity level of an ACPLC and that the ACPLC identified by this methodology is useful for bibliometric analyses.     

Google Scholar and Web of Science: Examining gender differences in citation coverage across five scientific disciplines

Many studies demonstrate differences in the coverage of citing publications in Google Scholar (GS) and Web of Science (WoS). Here, we examine to what extent citation data from the two databases reflect the scholarly impact of women and men differently. Our conjecture is that WoS carries an indirect gender bias in its selection criteria for citation sources that GS avoids due to criteria that are more inclusive. Using a sample of 1250 U.S. researchers in Sociology, Political Science, Economics, Cardiology and Chemistry, we examine gender differences in the average citation coverage of the two databases. We also calculate database-specific h-indices for all authors in the sample. In repeated simulations of hiring scenarios, we use these indices to examine whether women's appointment rates increase if hiring decisions rely on data from GS in lieu of WoS. We find no systematic gender differences in the citation coverage of the two databases. Further, our results indicate marginal to non-existing effects of database selection on women's success-rates in the simulations. In line with the existing literature, we find the citation coverage in WoS to be largest in Cardiology and Chemistry and smallest in Political Science and Sociology. The concordance between author-based h-indices measured by GS and WoS is largest for Chemistry followed by Cardiology, Political Science, Sociology and Economics.