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Pair stability refers to the extent to which exchange occurs between the same actors over time. In a stable pair, actors know what to expect of one another and have a sense of predictability as to the outcome of the exchange. When actors are split into discrete groups, shared group membership contributes to formation of new ties and maintenance of existing ties due to the mechanism of attraction to similar others. Using the formal framework of biased net theory, we propose three hypotheses which link shared group membership with the odds of pair stability. These hypotheses are tested against data from an experiment (N = 180) in which participants were first split into two groups and then given a series of opportunities to share resources with one another. Results of the experiment are consistent with the hypotheses. [ABSTRACT FROM AUTHOR]

Programs seeking to transform undergraduate science, technology, engineering, and mathematics courses often strive for participating faculty to share their knowledge of innovative teaching practices with other faculty in their home departments. Here, we provide interview, survey, and social network analyses revealing that faculty who use innovative teaching practices preferentially talk to each other, suggesting that greater steps are needed for information about innovative practices to reach faculty more broadly. [ABSTRACT FROM AUTHOR]

Military Chaplains are a critical component of behavioral health and spiritual support in combat operations. Support of combat operations has taken a toll on these caregivers. The purpose of this study was to explore the impact of deployment on the psychosocial and health characteristics and reintegration of Army National Guard (ARNG) chaplains. Seventy-four ARNG chaplains participated in an anonymous, online survey. Results were categorized into two mutually exclusive groups, combat deployed and non-combat deployed. Although both groups tended to present similar results, Combat deployed group chaplains were significantly more likely to be of higher rank, have served in a pastoral role in the ARNG longer, and present with higher scores for combat exposure, resilience, and alcohol use. Further, five and seven participants, respectively, the majority of whom were from the combat deployed group, endorsed “frequently” or “a great deal” to negative religious coping. These endorsements of abandonment may relate back to Reserve component specific deployment concerns. [ABSTRACT FROM PUBLISHER]

Controlled clinical trials are widely considered to be the vehicle to treatment discovery in cancer that leads to significant improvements in health outcomes including an increase in life expectancy. We have previously shown that the pattern of therapeutic discovery in randomized controlled trials (RCTs) can be described by a power law distribution. However, the mechanism generating this pattern is unknown. Here, we propose an explanation in terms of the social relations between researchers in RCTs. We use social network analysis to study the impact of interactions between RCTs on treatment success. Our dataset consists of 280 phase III RCTs conducted by the NCI from 1955 to 2006. The RCT networks are formed through trial interactions formed i) at random, ii) based on common characteristics, or iii) based on treatment success. We analyze treatment success in terms of survival hazard ratio as a function of the network structures. Our results show that the discovery process displays power law if there are preferential interactions between trials that may stem from researchers' tendency to interact selectively with established and successful peers. Furthermore, the RCT networks are ''small worlds'': trials are connected through a small number of ties, yet there is much clustering among subsets of trials. We also find that treatment success (improved survival) is proportional to the network centrality measures of closeness and betweenness. Negative correlation exists between survival and the extent to which trials operate within a limited scope of information. Finally, the trials testing curative treatments in solid tumors showed the highest centrality and the most influential group was the ECOG. We conclude that the chances of discovering life-saving treatments are directly related to the richness of social interactions between researchers inherent in a preferential interaction model. [ABSTRACT FROM AUTHOR]

Sociologists have begun to explore the gains for theory and research that might be achieved artificial intelligence technology: symbolic processors, expert systems, neural networks, genetic algorithms, and classifier systems. The first major accomplishments of artificial social intelligence (ASI) have been in the realm of theory, where these techniques have inspired new theories as well as helping to render existing theories more rigorous. Two application areas for which ASI holds great promise are the sociological analysis of written texts and data retrieval from the forthcoming Global Information Infrastructure. ASI has already been applied to some kinds of statistical analysis, but how competitive it will be with more conventional techniques remains unclear. To take advantage of the opportunities offered by ASI, sociologists will have to become more computer literate and will have to reconsider the place of programming and computer science in the sociological curriculum. ASI may be a revolutionary approach with the potential to rescue sociology from the doldrums into which some observers believe it has fallen. [ABSTRACT FROM AUTHOR]