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Abstract The aim of the study was to study how partners in heterosexual relationship do gender in interaction. The study was guided by the research questions; In what ways is gender done in interaction?; What different norms become visible?; And, what interactional consequences does gender construction lead to? An ethnomethodological perspective was adopted in which the way people in everyday life creates a common understanding of the world is focused. In line with the perspective gender was assumed to be a social construction created when people interact. Eight extracts from TV-interviews with couples was transcribed and analyzed using Membership categorization analysis (MCA). The chosen analytical method focuses on interaction participant’s referral to and use of membership categories and to these associated character traits and actions. The analysis showed that gender was created through explicit as well as implicit referrals to membership categories and character traits and actions associated to these. The gender norms that was made visible was the woman’s responsibility of home and family and the mail conqueror. Doing gender resulted in several consequences; (1) the maintenance/risk of maintenance of unequal division of responsibilities within couples/family constructions, were the greatest responsibility for family and home falls on the woman; (2) that the women sometimes was positioned, many times implicitly, in inferior gender roles; (3) that gender-based alliances could be created among the interaction participants; and (4) that statements, questions and answers could contain references to gender categories in ways that they appeared to be common knowledge and could function as support to what was sad. As the analysis also showed that gender categorization was a common feature associated with participants either holding themselves or others accountable it appears to be a topic for future research.

The present study employed a novel approach to extend current knowledge of how ideal leader prototypes and self-concepts solely and dually influence leader categorization and effectiveness judgments. Cluster analysis and policy-capturing were employed to examine independent and dependent variables as patterns. Findings partially supported hypotheses and corroborated previous research. Leader categorization and effectiveness judgments were self-affirming across multiple managerial performance scenarios; implicit policies varied based on the pattern of traits exhibited within their self-concepts and ideal leader prototypes. On average, people who endorsed prototypical ideal leader prototypes and self-concepts were more stringent compared to individuals with less prototypical patterns. They categorized fewer managers as leaders, perceived them as less effective, and weighed Planning, Motivating, and Controlling performance behaviors more in their judgments. The study also showed ideal leader prototypes explained variance in implicit policies for leader categorization and effectiveness beyond the variance accounted for by self-concepts; however, the self-concept remained a significant predictor of implicit policies for leader effectiveness. This novel finding suggests the self-concept, like the ideal leader prototype, is relevant in weighting performance behaviors for effectiveness judgment. / Ph. D.

With the growing number of textual resources available, the ability to understand them becomes critical. An essential first step in understanding these sources is the ability to identify the parts-of-speech in each sentence. The goal of this research is to propose, improve, and implement an algorithm capable of finding terms (words in a corpus) that are used in similar ways--a term categorizer. Such a term categorizer can be used to find a particular part-of-speech, i.e. nouns in a corpus, and generate a lexicon. The proposed work is not dependent on any external sources of information, such as dictionaries, and it shows a significant improvement (~30%) over an existing method of categorization. More importantly, the proposed algorithm can be applied as a component of an unsupervised part-of-speech tagger, making it truly unsupervised, requiring only unannotated text. The algorithm is discussed in detail, along with its background, and its performance. Experimentation shows that the proposed algorithm performs within 3% of the baseline, the Penn-TreeBank Lexicon. / Master of Science

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Denna uppsats bidrar till forskningen om social och språklig kategorisering i interaktion genom att undersöka kategoriseringsmetoder i serviceinteraktioner. Uppsatsen undersöker vilka metoder personal, kunder och personal och kunder tillsammans använder sig av för att identifiera kunderna som pensionärer i samband med rabatt, samt vilka metoder som används för att hantera aspekter av kategoriseringen pensionär som inte har med serviceärendet i sig att göra. Teoretiskt och metodologiskt bygger undersökningen på Conversation Analysis (CA), Membership Categorization Analysis (MCA) och multimodal interaktionsanalys. Studiens data består av 18 videoinspelade, transkriberade samtal vid teaterkassor. I studien identifieras och analyseras tre metoder för kategorisering: alternativa kategorier, överlämnande självkategorisering och retrospektiv nyansering. Analysen av metoderna visar bland annat att olika typer av deltagare behandlar olika aspekter av kategoriseringen som olika känsliga och olika relevanta, att deltagarna orienterar sig mot kategoriseringen som ett gemensamt projekt och att det vid vissa skeden i de i för övrigt målfokuserade servicesamtalen finns utrymme för kunder som kategoriserats som pensionärer att ge uttryck för personliga nyanseringar och positioneringar i förhållande till kategorin pensionär.

Notre étude est bipartite. En premier lieu nous avons effectué une étude empirique des différences entre les processus de catégorisation explicite (verbalisable) et implicite (non-verbalisable). Nous avons examiné la difficulté et le temps nécessaire pour apprendre trois tâches de catégorisation dites par air de famille, par règle logique conjonctive et par règle logique disjonctive. Nous avons ensuite utilisé un réseau neuronal pour modéliser la catégorisation en lui faisant compléter les mêmes tâches. La comparaison entre les deux nous permet de juger de l’adéquation du modèle. Les données empiriques ont montré un effet de la typicité et de la familiarité en accord avec la documentation et nous trouvons que la tâche de catégorisation par règle disjonctive est la plus difficile alors que la tâche de catégorisation par air de famille est la plus facile. La modélisation par le réseau est une réussite partielle mais nous présentons des solutions afin qu’un réseau futur puisse modéliser le processus catégoriel humain efficacement / Our present research was twofold. First, we conducted a study of the differences in the categorization processes between explicit (verbalizable) and implicit (nonverbalizable) tasks. The differentiation was done in term of difficulty and time necessary to learn the rule of the category for family resemblance, conjunctive and disjunctive rules. We then used a neural network to model the categorization and had it complete the same tasks. The comparison between the two gave us insight into how (and if) the network can be used as a model of human categorization. The empirical data confirmed an effect of familiarity and typicality, as supported by previous studies, and we confirmed that the disjunctive task was the hardest to learn for humans while our results point toward the family resemblance task as being the easiest. The modelization by the neural network was partially successful at best but we present options that could permit a next generation neural network to model the categorization process truthfully