This paper examines the mechanisms for giving by investigating the psychological and values differences between men and women's motivations for giving. We explored two of the eight mechanisms for giving developed by Bekkers and Wiepking as a framework for why people give-principle of care and empathic concern. Are there differences in these motives for giving by gender, and can these differences in values and the psychological benefits that people receive when making donations explain gender differences in charitable giving? Are women more likely to give and give more than men because of their higher levels of empathic concern and principle of care? We used two US national data sets to test our hypotheses. Our results for both data sets indicate significant differences in motives by gender, as well as differences in the probability of giving and amount given by gender, even after controlling for empathic concern and principle of care measures. Our findings are discussed in terms of the importance of viewing charitable giving through a gender lens as well as practical implications for practitioners.

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Scalability and performance implications of semantic net visualization techniques are open research challenges. This paper focuses on developing a visualization technique that mitigates these challenges. We present a novel approach that exploits the underlying concept of power-law degree distribution as many realistic semantic nets seems to possess a power law degree distribution and present a small world phenomenon. The core concept is to partition the node set of a graph into power and non-power nodes and to apply a modified force-directed method that emphasizes the power nodes which results in establishing local neighborhood clusters among power nodes. We also made refinements in conventional force-directed method by tuning the temperature cooling mechanism in order to resolve 'local-minima' problem. To avoid cluttered view, we applied semantic filtration on nodes, ensuring zero loss of semantics. Results show that our technique handles very large scale semantic nets with a substantial performance improvement while producing aesthetically pleasant layouts. A visualization tool, NavigOWL, is developed by using this technique which has been ported as a plug-in for Protege, a famous ontology editor.