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This article expands understanding of gender and advancement in academic science by going into a new dimension of inquiry: Focusing on women associate professors in computing, it assesses the relationship between perceived chances for promotion to full professor and indicators of entrepreneurship, as part of key sets of individual and departmental independent variables that are also addressed. Data from a national survey of women in academic computing indicate that time spent in entrepreneurial activity does not predict excellent/good (compared to fair/poor) chances for promotion perceived by these women faculty, nor does the quantity/quality of entrepreneurial activity that they report for their home units. Departmental reward structures reported as favoring entrepreneurial activity negatively predict perceived chances for promotion. Other key individual and departmental characteristics also predict chances for promotion: faculty members' age, collaboration, family characteristics, departmental climate, and US (compared to Canadian) location. Findings from interviews with a small subset of respondents to the survey illuminate the survey findings on the role of entrepreneurial factors in perceived chances for advancement. [ABSTRACT FROM AUTHOR]

Canada is a large nation with forested ecosystems that occupy over 60% of the national land base, and knowledge of the patterns of Canada's land cover is important to proper environmental management of this vast resource. To this end, a circa 2000 Landsat-derived land cover map of the forested ecosystems of Canada has created a new window into understanding the composition and configuration of land cover patterns in forested Canada. Strategies for summarizing such large expanses of land cover are increasingly important, as land managers work to study and preserve distinctive areas, as well as to identify representative examples of current land-cover and land-use assemblages. Meanwhile, the development of extremely efficient clustering algorithms has become increasingly important in the world of computer science, in which billions of pieces of information on the internet are continually sifted for meaning for a vast variety of applications. One recently developed clustering algorithm quickly groups large numbers of items of any type in a given data set while simultaneously selecting a representative-or 'exemplar'-from each cluster. In this context, the availability of both advanced data processing methods and a nationally available set of landscape metrics presents an opportunity to identify sets of representative landscapes to better understand landscape pattern, variation, and distribution across the forested area of Canada. In this research, we first identify and provide context for a small, interpretable set of exemplar landscapes that objectively represent land cover in each of Canada's ten forested ecozones. Then, we demonstrate how this approach can be used to identify flagship and satellite long-term study areas inside and outside protected areas in the province of Ontario. These applications aid our understanding of Canada's forest while augmenting its management toolbox, and may signal a broad range of applications for this versatile approach. [ABSTRACT FROM AUTHOR]