Although expressing positive emotions is typically socially rewarded, in the present work, we predicted that people suppress positive emotions and thereby experience social benefits when outperformed others are present. We tested our predictions in three experimental studies with high school students. In Studies 1 and 2, we manipulated the type of social situation (outperformance vs. non-outperformance) and assessed suppression of positive emotions. In both studies, individuals reported suppressing positive emotions more in outperformance situations than in non-outperformance situations. In Study 3, we manipulated the social situation (outperformance vs. non-outperformance) as well as the videotaped person’s expression of positive emotions (suppression vs. expression). The findings showed that when outperforming others, individuals were indeed evaluated more positively when they suppressed rather than expressed their positive emotions, and demonstrate the importance of the specific social situation with respect to the effects of suppression.
This paper presents a supervised classification model, where the indicators of correlation between dependent and independent variables within each class are utilized for a transformation of the large-scale input data to a lower dimension without loss of recognition relevant information. In the case study, we use the consumption data recorded by smart electricity meters of 4200 Irish dwellings along with half-hourly outdoor temperature to derive 12 household properties (such as type of heating, floor area, age of house, number of inhabitants, etc.). Survey data containing characteristics of 3500 households enables algorithm training. The results show that the presented model outperforms ordinary classifiers with regard to the accuracy and temporal characteristics. The model allows incorporating any kind of data affecting energy consumption time series, or in a more general case, the data affecting class-dependent variable, while minimizing the risk of the curse of dimensionality. The gained information on household characteristics renders targeted energy-efficiency measures of utility companies and public bodies possible.
Within the last decade, several studies have analysed the life writing produced by veterans of the U.S. American war against and the ensuing occupation of Iraq (2003-2011). These studies are beginning to offer insightful analyses of several autobiographies written by Iraq War veterans, they focus almost exclusively on male combat veterans, perpetuating a bias that has shaped the genre of autobiographies of war in the United States and the subsequent scholarly analyses of these autobiographies for decades, if not centuries. My paper begins to address this bias by focusing on two autobiographies by female soldiers, namely Jane Blair’s Hesitation kills (2011) and Heidi Squier Kraft’s Rule number two (2007). As the nineteenth-century biographies that Jo Burr Margadant has analysed, these texts can be considered ‘collage[s] of familiar notions merged in unfamiliar ways’ (Margadant, 2000, p.2), as both of them portray the experiences of female soldiers in a combat zone and thus trouble those assumptions about combat, identity, and gender that continue to structure more traditional American life writing about war. In my analysis I show how the autobiographical subjects in these texts are constructed through what I call ‘military femininities’. Drawing in part on conceptual and theoretical work done by Judith Butler, Leigh Gilmore, and R.W. Connell I argue that these military femininities are instrumental in creating female autobiographical subjects that are authorized to talk about war in the cultural context of the contemporary United States, and thus in establishing soldierly identities for American women more generally.