American historians and European archival theory: the collaboration of J.F. Jameson and Waldo G. Leland

J. Franklin Jameson and Waldo G. Leland remain forever linked in American archival history as the founders, respectively, of the National Archives and the American archival profession. From the immediate post-Revolutionary period until the eve of the First World War, American historians recognized that their nation's early history remained locked in European archives. However, not until the pioneering efforts of Leland, working under supervision of Jameson during the early 20th century, did any of these historical researchers bring to the United States, along with the voluminous copies of archival documents, the emerging tenets of European archival theory. This is the story of the collaboration between Jameson and Leland, and of Leland's efforts to professionalize American archival practice on the basis of European archival theory.     

Chinese Studies Librarianship in North America

Although American libraries had begun collecting Chinese language materials in the 19th century, notably the United States Library of Congress in 1869, Yale in 1878, Harvard in 1879, and Berkeley in 1896, East Asian studies librarianship in North America, including China studies librarianship, was not fully developed until the 1960s. There was no formal organization that represented the interest of Chinese studies librarians because there were few of them and most of them were China scholars rather than trained librarians. More than 100 years later, the number of Chinese studies librarians in North America has increased considerably, primarily in response to the demand in the field of China studies and more recently to the needs of immigrant population and the general public who has an interest in China.This paper traces the history and growth of Chinese studies librarianship in North America, documents the development of the professional organization that represents Chinese studies librarians, and exami.     

A study of clinical questions posed by hospital clinicians.

OBJECTIVE: This article presents the results of complementary research studies on the behaviors of hospital clinicians in asking clinical questions and the relationship between asking of questions, outcome of information searches, and success in problem solving. METHODS: Triangulation in research methods--a combination of mailed questionnaires, interviews, and a randomized controlled study--was employed to provide complementary views of the research problems under study. RESULTS: The survey and interviews found that clinical problems (concerned mainly with therapy and equipment or technology) were expressed as statements rather than questions (average number of concepts = 1.7), that only slightly more than half (higher for doctors) of problems could be solved, and that the majority of clinical questions were not well formed. An educational workshop however improved clinicians' formulation of questions, but the use of structured prompting was found to improve building of hypotheses in the doctors' group without training. The workshop also improved satisfaction with the obtained information and success in problem solving. Nonetheless, for both the experimental and control groups, more structured and complete questions or statements did not mean higher success rates in problem solving or higher satisfaction with obtained information. CONCLUSION: The triangulation methods have gathered complementary evidence to reject the hypothesis that building well-structured clinical questions would mean higher satisfaction with obtained information and higher success in problem solving.     

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.