摘要 :
In this paper we assess the relative importance of key academic factors - conference papers, journal articles and student supervisions - on the popularity of scholars in various knowledge areas, including areas of exact and biolog...
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In this paper we assess the relative importance of key academic factors - conference papers, journal articles and student supervisions - on the popularity of scholars in various knowledge areas, including areas of exact and biological sciences. To that end, we rely on curriculum vitae data of almost 700 scholars affiliated to 17 top quality graduate programs of two of the largest universities in Brazil, as well as popularity measures crawled from a large digital library, covering a 16-year period. We use correlation analysis to assess the relative importance of each factor to the popularity of individual scholars and groups of scholars affiliated to the same program. We contrast our results with those of two top programs of a major international institution, namely, the Computer Science and Medicine departments of the Stanford University.
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摘要 :
In this paper we assess the relative importance of key academic factors - conference papers, journal articles and student supervisions - on the popularity of scholars in various knowledge areas, including areas of exact and biolog...
展开
In this paper we assess the relative importance of key academic factors - conference papers, journal articles and student supervisions - on the popularity of scholars in various knowledge areas, including areas of exact and biological sciences. To that end, we rely on curriculum vitae data of almost 700 scholars affiliated to 17 top quality graduate programs of two of the largest universities in Brazil, as well as popularity measures crawled from a large digital library, covering a 16-year period. We use correlation analysis to assess the relative importance of each factor to the popularity of individual scholars and groups of scholars affiliated to the same program. We contrast our results with those of two top programs of a major international institution, namely, the Computer Science and Medicine departments of the Stanford University.
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摘要 :
In this paper, we analyze a ranking of the most "popular" scholars working in Brazilian institutions. The ranking was built by first sorting scholars according to their h-index (based on Google scholar) and then by their total cit...
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In this paper, we analyze a ranking of the most "popular" scholars working in Brazilian institutions. The ranking was built by first sorting scholars according to their h-index (based on Google scholar) and then by their total citation count. In our study, we correlate the positions of these top scholars with various academic features such as number of publications, years after doctorate, number of supervised students, as well as other popularity metrics. Moreover, we separate scholars by knowledge area so as to assess how each area is represented in the ranking as well as the importance of the academic features on ranking position across different areas. Our analyses help to dissect the ranking into each area, uncovering similarities and differences as to the relative importance of each feature to scholar popularity as well as the correlations between popularity metrics across knowledge areas.
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摘要 :
In this paper, we analyze a ranking of the most "popular" scholars working in Brazilian institutions. The ranking was built by first sorting scholars according to their h-index (based on Google scholar) and then by their total cit...
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In this paper, we analyze a ranking of the most "popular" scholars working in Brazilian institutions. The ranking was built by first sorting scholars according to their h-index (based on Google scholar) and then by their total citation count. In our study, we correlate the positions of these top scholars with various academic features such as number of publications, years after doctorate, number of supervised students, as well as other popularity metrics. Moreover, we separate scholars by knowledge area so as to assess how each area is represented in the ranking as well as the importance of the academic features on ranking position across different areas. Our analyses help to dissect the ranking into each area, uncovering similarities and differences as to the relative importance of each feature to scholar popularity as well as the correlations between popularity metrics across knowledge areas.
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摘要 :
Researchers who start delving into a new research area often resort to reading lists of scientific articles in order to familiarize themselves with the existing literature. Generally, these reading lists are manually built and rec...
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Researchers who start delving into a new research area often resort to reading lists of scientific articles in order to familiarize themselves with the existing literature. Generally, these reading lists are manually built and recommended by experts. In this paper we tackle the problem of automatically generating these lists. We start by stating five requirements for a comprehensive initial reading list, four of which were previously proposed and one is a contribution of ours. We then assess the extent to which these requirements are redundant or complementary. By performing a correlation analysis on a large dataset, we find that the five requirements are indeed mostly conflicting, which suggests that simultaneously meeting all of them is a difficult task. We then perform an extensive set of experiments, comparing twenty-five different approaches to automatically generate initial reading lists, most of which are new proposals of ours which exploit learning to rank (L2R) and aggregation methods to combine multiple pieces of evidence and objectives. Our experimental results indicate that, though no method outperforms the others in all five requirements, our new L2R and aggregation methods significantly outperform the state-of-the-art when jointly considering all requirements. Moreover, we identify a subset of six new techniques which offer the best tradeoff (in a Pareto-efficient sense) across all five requirements.
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摘要 :
Researchers who start delving into a new research area often resort to reading lists of scientific articles in order to familiarize themselves with the existing literature. Generally, these reading lists are manually built and rec...
展开
Researchers who start delving into a new research area often resort to reading lists of scientific articles in order to familiarize themselves with the existing literature. Generally, these reading lists are manually built and recommended by experts. In this paper we tackle the problem of automatically generating these lists. We start by stating five requirements for a comprehensive initial reading list, four of which were previously proposed and one is a contribution of ours. We then assess the extent to which these requirements are redundant or complementary. By performing a correlation analysis on a large dataset, we find that the five requirements are indeed mostly conflicting, which suggests that simultaneously meeting all of them is a difficult task. We then perform an extensive set of experiments, comparing twenty-five different approaches to automatically generate initial reading lists, most of which are new proposals of ours which exploit learning to rank (L2R) and aggregation methods to combine multiple pieces of evidence and objectives. Our experimental results indicate that, though no method outperforms the others in all five requirements, our new L2R and aggregation methods significantly outperform the state-of-the-art when jointly considering all requirements. Moreover, we identify a subset of six new techniques which offer the best tradeoff (in a Pareto-efficient sense) across all five requirements.
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