Organizational Power and the Institutional Review Board

In this essay, we respond to the narratives and essays in this issue by examining organizational power issues related to researchers and institutional research boards (IRBs). Rather than taking a singular perspective on power, we use multiple perspectives and suggest that the cumulative analyses provide the best understanding of IRBs and organizational power.     

Setting the Students’ Bearings and Balance in Appraisal

The author, a professor at the University of Toronto, touches briefly on the extensive and rich archival literature that supports the teaching of macroappraisal, but notes that this is not the only educational material she offers her students when teaching appraisal theory. She discusses the usefulness to archivists of literature from the fields of ethnography, organizational knowing, records in history, personal documentary behaviour, memory, and communications, noting that the use of texts from these fields can encourage students to reflect on their own presumptions and to develop a taste for the wide reading and research that must support appraisal.     

Online Celebrity Bashing: Wrecking Ball or Good for You? Adolescent Girls’ Attitudes Toward the Media and Public Bashing of Miley Cyrus and Selena Gomez

Journalists and readers of celebrity news regularly bash celebrities online, a behavior that is easily accepted among adolescents. This study investigates whether these attitudes of acceptance differ according to the perpetrator of the bashing (media versus public) and the likeability of the involved celebrity (liked versus disliked). Using a vignette study, we examine adolescent girls’ attitudes toward media (journalists’) and public (readers’) bashing of a generally disliked celebrity (Miley Cyrus) and a generally liked celebrity (Selena Gomez). All participants read an identical negative news story (media bashing) and two related negative reader comments (public bashing). Participants were randomly assigned to read this information about either Miley Cyrus or Selena Gomez. Results of a mixed-design ANOVA showed that the girls had less negative attitudes toward media bashing compared with public bashing. Moreover, they more easily accepted the bashing of a disliked celebrity than the bashing of a liked celebrity.     

The Librarian BFF: A Case Study of a Cohort-Based Personal Librarian Program

This case study presents the development and implementation of a personal librarian program for a cohort-based, executive-style higher education administration doctoral program. Librarians and the program director collaborated to create a student-centered program based on individual research needs. The personal librarian program was designed to build a close relationship between the librarian and individual students; to help identify their research knowledge gaps; and to identify resources to meet each individual students' research needs.     

Using word embeddings in Twitter election classification

Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).     

Teaching PEACE: A plan for effective crisis communication instruction

Courses: This unit activity is intended for public relations (PR), crisis communication, or journalism courses.

Objectives: The purpose is to equip future PR professionals with critical thinking skills and experience to manage crises. Students demonstrate mastery in two ways: by crafting clear crisis response messages and materials in a narrow time frame, and by applying a crisis communication heuristic to manage a simulated crisis event.     

Investigating the quality of interactions and public engagement around scientific papers on Twitter

This study explores science communication on Twitter by investigating a sample of tweets referring to academic papers in five different scientific fields. The specifications of science communicators on Twitter, the characteristics of those who initiate actions (by tweeting), the extent and quality of reactions (retweeting), individual and group interactions, and the distribution of tweets across types of engagement in the process of science communication (i.e., dissemination, consultation, and evaluation) were explored. A broad array of actors is involved in the communication of science on Twitter, with individual citizens and individual researchers playing an important role. In principle, this is promising for creating direct interaction, which can be difficult through more traditional mass media. The vast majority of communication activities regarding academic papers is undigested dissemination with almost no sign of debate, contestation, or collective reflection. Another general finding of this study is that bot accounts play a major role in the science communication landscape on Twitter.