Paving Consensus: Enacting, Challenging, and Revising the Consensus Process in a Cohousing Community

This study focused on a cohousing community's use of consensus to make a decision about surfacing a parking area. It revealed that the community's use of consensus decision making allowed the residents to balance three goals: making an appropriate decision, meeting members' needs, and maintaining the community's well-being. Reaching agreement, however, was complicated by members' value differences and discontinuity in their participation. The analysis of this case reveals three qualities characteristic of the enactment of consensus: the role of structured communication within and between group meetings, a tension between maintaining process openness and reaching decision closure, and the expectation that group members will work within the consensus process. The analysis also highlights the importance of timing in the interpretation of conflict in a consensus-oriented group and the role of process change when a group reaches the limits of members' commitment to consensus.     

Employee mistreatment and muted voices in the culturally diverse workplace

This study examines mistreatment through the perspectives of employees with different cultural backgrounds and positions in order to understand the ways in which some voices are muted and others are privileged. Mistreatment is interactional, distributive, procedural, or systemic abuse of employees taking place at both interpersonal and institutional levels on the basis of cultural diversity. Narrative analysis of semi-structured interviews with employees of a large research and development organization revealed three types of muted narratives used to respond to mistreatment (muted-but-engaged, angrily disengaged, and resigned) as well as one type of privileged response. The narratives provided evidence of two processes by which voices became muted: repeated silencing over time and silencing through ambiguity of policies.     

Books are silent ambassadors: The Noma award for the translation of Japanese literature

Summary Now more than ever good translations are proving to be vitally important to fulfilling Shoichi Noma’s mission of advancing good relationships between cultures. With the emergence of dozens of world-class Japanese writers, the translators of Japanese literature have acquired a new significance and a new importance. No longer are they required to simply handle the language like an exotic and precious commodity but they must prepare a writer’s work to compete with literature from around the globe and let others understand the issues at their very heart of the Japanese people. Indeed, the selection committee’s choice of Murakami’s Wind-Up Bird Chronicle is an apt example of how literature at its very best can span cultural divides to promote mutual understanding of very complicated and important world issues. Written for Japanese to help them understand the devastation of war, it is a rare and timely book that confronts Japan’s experience head on. Ironically, Noma’s idea that books are silent ambassadors, an idea born from agony and defeat, so many years later is helping the world come together towards a unified vision of peace and understanding. “Literature has been described as a means of exchange between the hearts of people. Literary translation, then, is the building of bridges so that all peoples of the world can interact in this way.” Ye Weiqu, Chinese Institute of Social Studies.     

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.