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     

Character N-Gram Tokenization for European Language Text Retrieval

The Cross-Language Evaluation Forum has encouraged research in text retrieval methods for numerous European languages and has developed durable test suites that allow language-specific techniques to be investigated and compared. The labor associated with crafting a retrieval system that takes advantage of sophisticated linguistic methods is daunting. We examine whether language-neutral methods can achieve accuracy comparable to language-specific methods with less concomitant software complexity. Using the CLEF 2002 test set we demonstrate empirically how overlapping character n-gram tokenization can provide retrieval accuracy that rivals the best current language-specific approaches for European languages. We show that n = 4 is a good choice for those languages, and document the increased storage and time requirements of the technique. We report on the benefits of and challenges posed by n-grams, and explain peculiarities attendant to bilingual retrieval. Our findings demonstrate clearly that accuracy using n-gram indexing rivals or exceeds accuracy using unnormalized words, for both monolingual and bilingual retrieval.     

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.     

Validating the Willingness to Self-Censor Scale: Individual Differences in the Effect of the Climate of Opinion on Opinion Expression

Hayes, Glynn, and Shanahan (2005) defined self-censorship asthe withholding of one’s opinion around an audience perceivedto disagree with that opinion. They argued that people differin their willingness to self-censor and introduced an 8-itemself-report instrument, the Willingness to Self-Censor scale,to measure this individual difference. The results of an experimentalstudy presented here provide further evidence of the constructvalidity of the scale. Each participant in the study was presentedwith a hypothetical scenario that contained information suggestinga group of people the participant was conversing with abouta controversial topic held opinions that were either uniformlysimilar to or different from the participant’s own opinion.Four weeks prior, each participant had responded to the Willingnessto Self-Censor scale and a measure of dispositional shyness.As expected, the manipulation of the climate of opinion affectedwillingness to express an opinion to the group, but more soamong those who scored relatively high on the Willingness toSelf-Censor scale. These results support the notion that somepeople rely on information about the climate of opinion moreso than do others when they decide whether or not to voice theiropinion publicly, and they suggest that the Willingness to Self-Censorscale measures this individual difference.     

Storytime with Fresh Professor,Part One

I wasn't always the Fresh Professor. At one point, I was just another starving actor, trying to make a living. But stories change over time, as do professional desires.