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.     

Excluding archival silences: Oral history and historical absence

Despite our society's increasing reliance on electronic documentation, to-date archives remain, largely, material repositories of cultural memory. It is an accepted historical problematic, however, that culture is often resistant to material preservation. There exists an undeniable and profound tension between scholarly efforts to reconstruct history and interpret cultural traditions and the fragmentary, and often limited, material record. That is to say, scholarship is shaped by a sinuous negotiation around the historical silences that encompass all of material culture. Historical silences, however, can at times be marginalized (or at best excluded) by a sensitive configuration of material evidence with oral history.Excluding Archival Silences: Oral History and Historical Absence uses a historically and geographically specific example of oral history to engage in a more generalist discussion of how oral reflection, especially when shaped by material evidences, can be an especially effective tool for preserving the dynamics of culture that often remain undocumented.     

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.     

Automatic Alphabet Recognition

The last step of the Information Retrieval process is to display the found documents to the user. However, some difficulties might occur at that point. English texts are usually written in the ASCII standard. Unlike the English language, many languages have different character sets, and do not have one standard. This plurality of standards causes problems, especially in a web environment, where one may download a document with an unknown standard. This paper suggests a purely automatic way of finding the standard which was used by the document writer based on the statistical letters distribution in the language. We developed a vector-space-based method that creates frequencies vectors for each letter of the language and then matches a new document's vectors to the pre-computed templates. The algorithm was applied on various types of corpora in Hebrew, Russian and English, and provides an efficient solution to the stated problem in most cases.