Explore the vast range of titles available.

An avalanche of recent newspapers, weekly newsmagazines, scholarly journals, and academic books has helped to spark a heated debate by publishing warnings of a \"boy crisis\" in which male students at all academic levels have begun falling behind their female peers. InLearning the Hard Way, Edward W. Morris explores and analyzes detailed ethnographic data on this purported gender gap between boys and girls in educational achievement at two low-income high schools-one rural and predominantly white, the other urban and mostly African American. Crucial questions arose from his study of gender at these two schools. Why did boys tend to show less interest in and more defiance toward school? Why did girls significantly outperform boys at both schools? Why did people at the schools still describe boys as especially \"smart\"? Morris examines these questions and, in the process, illuminates connections of gender to race, class, and place. This book is not simply about the educational troubles of boys, but the troubled and complex experience of gender in school. It reveals how particular race, class, and geographical experiences shape masculinity and femininity in ways that affect academic performance. His findings add a new perspective to the \"gender gap\" in achievement.

Clustering is a significant unsupervised learning method in the machine learning field, which can mine the distribution pattern and attribute of data. However, traditional clustering methods can not fully represent the attribution relationship between objects and classes. Therefore, a three-way clustering (3WC), which combines three-way decision (3WD) with clustering, has gradually received widespread attention from researchers in recent years. However, existing 3WC methods mostly use traditional clustering results or randomly assigned results as initial division results, which largely ignore the distribution relation of each object. Moreover, most of 3WC methods are soft clustering, i.e., there are some objects that will belong to more than one class, which makes clustering results more ambiguous. In light of this situation, we establish a feature distribution-based adaptive three-way clustering (3WC-D) method to address the above challenge. First, 3WC-D utilizes 3WD to characterize the distribution relation of objects for obtaining initial clustering results. Then, several representative classes are selected for further processing based on the interrelationship among classes in initial clustering results. Finally, the remaining objects are divided according to the relative relation between objects and classes, so as final clustering results can be obtained, and the effectiveness of the method is illustrated by comparing with several clustering methods on diverse datasets.