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The primary significance of this inquiry is as a contribution to the continuing effort to categorize socialization studies and thereby to make more useful the accumulation of empirical knowledge in the field. This thesis presents a summary of sociological studies of socialization in the 1960's. It reviews articles published between January 1, 1960 and December 31, 1967.

Driving simulators are a main way researchers gather data about on-the-road behavior. However, the quantity of data produced by these simulators forces researchers to rely on algorithms to aid in cleaning and analyzing the data. One example of this is defining whether the vehicle is making a lane change or turning a corner by broadly categorizing the angle of the steering wheel. A more precise method of identifying these driving maneuvers is described. This method involves using self-organizing maps to consider multiple aspects of user input when determining the existence of a lane change or turn. The results show that while steering angle is the most relevant variable to consider, other variables such as throttle pressure can be used to improve the accuracy of the categorization. This indicates a need for further study into the automatic classification of driving simulator data.

The intent of this study is to investigate the effects of political polarization on the categorization and cognition of neutral stimuli. Polarization in politics is increasing and affecting the general electorate. This study was a within-subjects design with two stimuli conditions (slogans and personas). Both stimuli conditions had liberal, neutral, and conservative levels. The data was recorded using the Qualtrics survey software. The results of the classification and rating were compared across political party affiliation and political activity level. Analysis showed that there was a significant difference in the classification of neutral stimuli.

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In U.S. society, the systems of gender and race operate to privilege and oppress individuals based on their location within these systems. All of the interactions an individual experiences as they go about their day-to-day lives are shaped by these interlocking systems. As a result, there is an extensive body of sociological literature addressing how individuals in U.S. society are privileged and oppressed on the basis of their perceived membership in gender and race categories; however, relatively little research exists examining how individuals come to be seen by others as members of gender and race categories in the first place. In order to address this gap in the existent literature, this thesis asked 354 participants to perform gender and race categorizations for 28 target individuals of various gender and race category memberships. Participants were asked to make a categorization, rate how confident they were in that categorizations accuracy, and then explain why they made the gender or race categorization that they did. In analyzing these categorizations, this thesis produced three important findings about the process of gender and race categorization. First, this thesis identified two gender categories ("female" and "male") and eight race categories ("White," " Black," "Latino," "Asian," "Southeast Asian," "South Asian/Indian," "Middle Eastern," and "Mixed Race") used in gender and race categorization. Second, particularly in the common usage of the biologically-based concepts of "sex" and "race," rather than the socially-based concepts of "gender" and "ethnicity." Third, this thesis found interactions between the gender and race systems in categorization, finding that White individuals and male individuals are gender categorized more easily than Black individuals or female individuals, and individuals will less "ambiguous" skin coloration are more easily categorized than others.

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With the emergence and proliferation of Internet services and e-commerce applications, a tremendous amount of information is accessible online, typically as textual documents. To facilitate subsequent access to and leverage from this information, the efficient and effective management¡Xspecifically, text categorization¡Xof the ever-increasing volume of textual documents is essential to organizations and person. Existing text categorization techniques focus mainly on categorizing monolingual documents. However, with the globalization of business environments and advances in Internet technology, an organization or person often retrieves and archives documents in different languages, thus creating the need for cross-lingual text categorization. Motivated by the significance of and need for such a cross-lingual text categorization technique, this thesis designs a technique with two different category assignment methods, namely, individual- and cluster-based. The empirical evaluation results show that the cross-lingual text categorization technique performs well and the cluster-based method outperforms the individual-based method.

Text categorization deals with the automatic learning of a text categorization model from a training set of preclassified documents on the basis of their contents and the assignment of unclassified documents to appropriate categories. Most of existing text categorization techniques deal with monolingual documents (i.e., all documents are written in one language) during the text categorization model learning and category assignment (or prediction). However, with the globalization of business environments and advances in Internet technology, an organization or individual often generates/acquires and subsequently archives documents in different languages, thus creating the need for cross-lingual text categorization (CLTC). Existing studies on CLTC focus on the prediction-corpus translation-based approach that lacks of a systematic mechanism for reducing translation noises; thus, limiting their cross-lingual categorization effectiveness. Motivated by the needs of providing more effective CLTC support, we design a training-corpus translation-based CLTC approach. Using the prediction-corpus translation-based approach as the performance benchmark, our empirical evaluation results show that our proposed CLTC approach achieves significantly better classification effectiveness than the benchmark approach does in both Chinese