Elämänmuutoksen, jännityksen ja asiantuntijuuden representointi Veikkaus Oy:n mainonnassa vuosina 1970— 2006

Aim of this master's thesis paper for consumer economics, is to research gambling advertisements in Finland over a period of 35 years, from 1970 to 2006. Veikkaus Oy (later Veikkaus), was founded in 1940, as one of the three licensed gambling organizations in Finland. Material for the current research comprised 1494 advertisements published by Veikkaus in newspapers and magazines at that time. Veikkaus has the exclusive licence to organize lotto games, sport games, instant games and other draw games in Finland. The other two operators, The Finnish Slot Machine Association RAY and Fintoto (on-track horse betting), were not included in the current analysis. This study has been completed according to research contract and grand by the Finnish Foundation for Gaming Research (Pelitoiminnan tutkimussäätiö). In general, advertisements reflect surrounding culture and time, and their message is built on stratified meanings, symbols and codes. Advertising draws the viewer's attention, introduces the advertised subject, and finally, affects the individual's consumption habits. However, advertisements not only work on individual level, but also influence public perception of the advertised product. Firstly, in order to assess gambling as a phenomenon, this paper discusses gambling as consumer behaviour, and also reviews history of gambling in Finland. Winning is a major feature of gambling, and dreaming about positive change of life is a centre of most gambling ads. However, perceived excitement through risk of losing can also be featured in gambling ads. Secondly, this study utilizes Veikkaus’ large advertising archives, were advertising data is analyzed by content analysis and the semiotic analysis. Two methods have been employed to support analyzing outcome in a synergistic way. Content analysis helps to achieve accuracy and comprehensiveness. Semiotic analysis allows deeper and more sensitive analysis to emerged findings and occurrences. It is important to understand the advertised product, as advertising is bound to the culture and time. Hence, to analyze advertising, it is important to understand the environment where the ads appear. Content analysis of Veikkaus data discovered the main gambling and principal advertisement style for each.period. Interestingly, nearly half of Veikkaus’ advertisements promoted topic other than “just winning the bet”. Games of change, like Lotto, typically advertised indirectly represented dreams about winning. In the category of skill gambling, features were represented as investment, and the excitement of sporting expertise was emphasized. In addition, there were a number of gambling ads that emphasize social responsibility of Veikkaus as a government guided organization. Semiotic methods were employed to further elaborate on findings of content analysis. Dreaming in the advertisements was represented by the product of symbols, (e.g. cars and homes) that were found to have significance connection with each other. Thus, advertising represents change of life obtained by the winning. Interestingly, gambling ads promoting jackpots were often representing religious symbolisms. Ads promoting social responsibility were found to be the most common during economical depression of the 90’s. Deeper analysis showed that at that time, advertisements frequently represented depression-related meanings, such as unemployment and bank loans. Skill gaming ads were often represented by sports expertise – late 90’s, their number started sky rocketing, and continued increasing until 2006 (when this study ended). One may conclude that sport betting draws its meanings from the relevant consumer culture, and from the rules and features of the betted sport.

Patterns in permuted binary matrices

Reorganizing a dataset so that its hidden structure can be observed is useful in any data analysis task. For example, detecting a regularity in a dataset helps us to interpret the data, compress the data, and explain the processes behind the data. We study datasets that come in the form of binary matrices (tables with 0s and 1s). Our goal is to develop automatic methods that bring out certain patterns by permuting the rows and columns. We concentrate on the following patterns in binary matrices: consecutive-ones (C1P), simultaneous consecutive-ones (SC1P), nestedness, k-nestedness, and bandedness. These patterns reflect specific types of interplay and variation between the rows and columns, such as continuity and hierarchies. Furthermore, their combinatorial properties are interlinked, which helps us to develop the theory of binary matrices and efficient algorithms. Indeed, we can detect all these patterns in a binary matrix efficiently, that is, in polynomial time in the size of the matrix. Since real-world datasets often contain noise and errors, we rarely witness perfect patterns. Therefore we also need to assess how far an input matrix is from a pattern: we count the number of flips (from 0s to 1s or vice versa) needed to bring out the perfect pattern in the matrix. Unfortunately, for most patterns it is an NP-complete problem to find the minimum distance to a matrix that has the perfect pattern, which means that the existence of a polynomial-time algorithm is unlikely. To find patterns in datasets with noise, we need methods that are noise-tolerant and work in practical time with large datasets. The theory of binary matrices gives rise to robust heuristics that have good performance with synthetic data and discover easily interpretable structures in real-world datasets: dialectical variation in the spoken Finnish language, division of European locations by the hierarchies found in mammal occurrences, and co-occuring groups in network data. In addition to determining the distance from a dataset to a pattern, we need to determine whether the pattern is significant or a mere occurrence of a random chance. To this end, we use significance testing: we deem a dataset significant if it appears exceptional when compared to datasets generated from a certain null hypothesis. After detecting a significant pattern in a dataset, it is up to domain experts to interpret the results in the terms of the application.

Interruption and Uncooperativeness in Academic ELF Group Work : An Application of Linear Unit Grammar

This study addresses the challenge of analyzing interruption in spoken interaction. It begins with my observation of eight hours of academic group work among speakers of English as a lingua franca (ELF) in a university course. Unlike the common findings of ELF research which underscore the cooperative orientation of ELF users, this particular group gave strong impressions of interruption and uncooperativeness as they prepared a scientific group presentation. In the effort to investigate these impressions, I found that no satisfactory method exists for systematically identifying and analyzing interruptions. A useful tool was found in Linear Unit Grammar or LUG (Sinclair & Mauranen 2006), which analyzes spoken interaction prospectively as linear text. In the course of transcribing one of the early group work meetings, I developed a model of LUG-based criteria for identifying individual instances of interruption. With this system in place, I was then able to evaluate the aggregate occurrences of interruption in the group work and identify co-occurring interactive features which further influenced the perception of uncooperativeness. Finally, these aggregate statistics directed a return to the data and a contextually sensitive, qualitative analysis. This research cycle illuminates the interactive features which contributed to my own impressions of uncooperativeness, as well as the group members orientations to their own interruptive practice.