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.     

Museums at the crossroad: Contributing to dialogue, curiosity and wonder in natural history museums

The first natural history collections opened to the public were inspired by a sense of curiosity and wonder about the products of nature. They were ‘cabinets of curiosities’ that offered a first-hand interaction between owner and visitors. Nowadays, these two facets of the museum experience—dialogue and wonder—have been lost, in part, due to the information overload coming via the media and the impersonal nature of the museum visit. This paper offers some reflections on the evolution of the museum visit, suggests some ways to rediscover this ‘sense of wonder’ and provides ideas on how to promote two-way communication with museum visitors. Two examples of exhibitions are offered as illustrations of the points discussed.     

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.     

UNIMARC Format Relevance: Maintenance or Replacement?

This article presents an empirical study focused on a qualitative analysis of the UNIMARC format. An analysis of the structural quality of the data provided by the format is evaluated to determine its current suitability for meeting the requirements and trends in data architecture for the information network and the Semantic Web. Driven by a set of quality characteristics that identify weaknesses in the data schema that cannot be bridged by simply converting data to MARC XML or RDF/XML, we conclude that the UNIMARC format is not compliant with the current metadata schema desiderata and must be replaced.     

Antecedents and consequences of autonomous information seeking motivation

Autonomously motivated individuals tend to enjoy activities more than those who are extrinsically motivated, and they also tend to invest more effort. Grounded in basic psychological needs theory, the current study examines the motivation of students' autonomous information seeking and its relationship with the amount of effort invested in and enjoyment derived from information seeking. Autonomy support and perceived competence were found to explain 34.7% of the variance in autonomous motivation while autonomous motivation, in turn, explained 13.1% of variance in effort and 25.8% variance in enjoyment. The model indicates a positive relationship between basic psychological needs and autonomous motivation. Higher students' autonomy support and perceived competence levels lead to increases in autonomous motivation to seek information. Higher autonomous motivation levels, similarly, lead to higher levels of effort and enjoyment. Findings confirm that intrinsically motivated students enjoy information seeking more and invest more effort in the activity. This highlights the importance of encouraging students to engage in information seeking independently, while providing the necessary guidance that would increase their competence.