Who's next? Research predicts museum leadership gap

A brief commentary from the Museum Association of New York (MANY) on the unprecedented gap posed by baby-boomer retirement. Based, in part, on MANY's white paper, “Who's Next? Questioning the Future of Museum Leadership in New York State,” this essay deals with questions of succession, leadership and general differences in New York's museum community.     

Editorial

This issue of the Journal contains three interesting articlesabout central aspects of public opinion research, as well asan equal number of research notes that reflect the range oftopics that interest all public opinion researchers. Toshio Takeshita takes a turn at reconceptualizing agenda-settingstudies, using a taxonomy     

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.     

Analysis of Statistical Question Classification for Fact-Based Questions

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.     

Staff development planning in an academic health sciences library.

OBJECTIVE: A staff development committee (SDC) was convened to implement staff development opportunities for an academic health sciences library system comprised of three separate facilities. The charge for the SDC was to: (1) develop programs to enhance workplace skills and personal growth, (2) communicate the availability of existing programs at the university and medical center, and (3) encourage the staff to participate in these opportunities. PROGRAM: The committee created goals and objectives and developed a survey designed to give staff the opportunity to provide input for this initiative. With an 80% response rate, the survey results were used to plan 15 events based on staff needs and preferences. First-year attendance for SDC-sponsored events was 459. Committee members served as liaisons for each event. Two forms were developed to facilitate event planning. A monthly announcement sheet, email reminders, and the library's local area network are used to communicate upcoming SDC events and encourage attendance. CONCLUSION: This approach can serve as a useful model for similar program planning in any organization.     

Explaining Media Choice: The Role of Issue-Specific Engagement in Predicting Interest-Based and Partisan Selectivity

This study analyzes the predictors of 2 types of media selectivity: interest-based (i.e., choice of entertainment over politics) and partisan (i.e., choice of pro-attitudinal over counter-attitudinal or balanced news). Relying on a large survey-based experiment, we find that issue-specific engagement variables, including perceived issue understanding, issue importance, and issue attitude strength, predict interest-based and partisan selectivity above and beyond the influence of general political knowledge, news interest, and strength of political leanings. These results show that the drivers of selectivity are more complex than general political attributes; rather, they are contextual and reflect people’s engagement with particular issues.