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.     

Evolving wireless access technologies for municipal broadband

In recent years, the landscape for wireless technology has changed substantially, with profound implications for the evolution of last-mile access infrastructure. This paper provides a high-level introduction to emerging trends in wireless technology, with a special focus on how these are impacting municipal broadband deployments. Also discussed are some of the key architectural and design choices for wireless networking systems and their implications for cost and system performance. In addition, examples are provided regarding how the new wireless technologies are being deployed by municipalities in a variety of contexts, with reference to wireless technologies currently available from vendors. The policy implications of these trends are discussed further in a companion paper in this issue.     

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.     

Managing a Biomedical Library’s Instruction Program: Redefining Scope

The National Institutes of Health (NIH) library instruction program provides training services to staff, clinicians, and researchers across NIH and several Department of Health and Human Services (HHS) operating divisions. The audience, training needs, and training topics are diverse and constantly changing. The NIH library has developed several new services and class topics to meet the needs of its NIH and HHS communities. Context, strategies, and future directions for the NIH library instruction program are described.