Gender on the box: Self-reflexivity as a starting point

This self-reflexive activity acts as an introduction to how we talk about and express gender identity, as well as the assumptions we may have about gender identity norms and expression. The activity illuminates student’s subconscious behaviors and understandings of gender, pushing them to sit self-reflexively with their own understandings of gender as an identity, expression, binary, and potential locus of shame/freedom.

Courses: Introduction to Women and Gender Studies, Intercultural Communication, Media Studies, Gender and Communication, Performance Studies

Objectives: Designed to introduce students to their own understandings and embodiment of gender, this activity asks students to be honest about their preconceived notions regarding gender that they bring with them into the classroom. The activity utilizes predesigned components that test students’ subconscious knowledge of the gender binary. This is a one-time activity that can be conducted in one 50- or 75-minute class period.     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

A method for transcribing signed and spoken language

Transcribing sign communication used simultaneously with spoken English presents investigators with a unique problem: the singular quality of the bimodal communicative interactions cannot be accurately depicted using accepted conventions for recording either spoken or signed language samples. This article proposes guidelines for transcribing such data. The need for guidelines arose during an earlier study of the language development of a hearing child of deaf parents. To meet the immediate needs of that study, rules and conventions from previous studies were combined with newly generated ones, resulting in the guidelines proposed in this article. The guidelines can be applied to data in which intermodal linguistic influence is suspected.     

Book reviews

Zwartjes, Otto, ed. La Sociedad andalusí y sus tradiciones literarias (Foro Hispánico, 7). Amsterdam: Rodopi, 1994.

Brincat, Joseph M. Malta 870–1054: Al‐Himyai's Account and its Linguistic Implications. Malta: Said International Ltd., 1995. 52pp.

Sells, Michael A. Mystical Languages of Unsaying. Chicago: The University of Chicago Press, 1994. 316 pp., US$18.95 (paperback), US$49.91 (cloth).

Diem, Werner, Arabische Geschäftsbriefe des 10. bis 14. Jahrhunderts aus der Österreichischen Nationalbibliothek in Wien (Documents Arabica Antiqua 1), 2 vols. Wiesbaden: Harrassowitz, 1995. Textband ix+518pp., Tafelband 76 plates.

Coope, Jessica A. The Martyrs of Cordoba: Community and Family Conflict in an Age of Mass Conversion. Lincoln: University of Nebraska Press, 1995. xvii+113 pp., US$ 25 (cloth).

Edwards, John. Religion and Society in Spain, c.1492 (Variorum Collected Studies Series: CS 520). Aldershot and Brookfield: Variorum, 1996. x+351 pp., US$ 97.00 (cloth).

Tolan, John Victor, ed. Medìeval Christian Perceptions of Islam. A Book of Essays (Garland Medieval Casebooks, Volume 10). New York and London: Garland Publishing, 1996. xxi+414 pp., US$60.00 (cloth).     

Design and evaluation of a personal digital assistant- based alerting service for clinicians.

PURPOSE: This study describes the system architecture and user acceptance of a suite of programs that deliver information about newly updated library resources to clinicians' personal digital assistants (PDAs). DESCRIPTION: Participants received headlines delivered to their PDAs alerting them to new books, National Guideline Clearinghouse guidelines, Cochrane Reviews, and National Institutes of Health (NIH) Clinical Alerts, as well as updated content in UpToDate, Harrison's Online, Scientific American Medicine, and Clinical Evidence. Participants could request additional information for any of the headlines, and the information was delivered via e-mail during their next synchronization. Participants completed a survey at the conclusion of the study to gauge their opinions about the service. RESULTS/OUTCOME: Of the 816 headlines delivered to the 16 study participants' PDAs during the project, Scientific American Medicine generated the highest proportion of headline requests at 35%. Most users of the PDA Alerts software reported that they learned about new medical developments sooner than they otherwise would have, and half reported that they learned about developments that they would not have heard about at all. While some users liked the PDA platform for receiving headlines, it seemed that a Web database that allowed tailored searches and alerts could be configured to satisfy both PDA-oriented and e-mail-oriented users.     

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.