This paper elaborates how to identify and evaluate causal factors to improve scientific impact. Currently, analyzing scientific impact can be beneficial to various academic activities including funding application, mentor recommendation, and discovering potential cooperators etc. It is universally acknowledged that high-impact scholars often have more opportunities to receive awards as an encouragement for their hard working. Therefore, scholars spend great efforts in making scientific achievements and improving scientific impact during their academic life. However, what are the determinate factors that control scholars' academic success? The answer to this question can help scholars conduct their research more efficiently. Under this consideration, our paper presents and analyzes the causal factors that are crucial for scholars' academic success. We first propose five major factors including article-centered factors, author-centered factors, venue-centered factors, institution-centered factors, and temporal factors. Then, we apply recent advanced machine learning algorithms and jackknife method to assess the importance of each causal factor. Our empirical results show that author-centered and article-centered factors have the highest relevancy to scholars' future success in the computer science area. Additionally, we discover an interesting phenomenon that the h-index of scholars within the same institution or university are actually very close to each other.
翻译:本文阐述了如何确定和评估因果因素以提高科学影响。目前,分析科学影响可能有益于各种学术活动,包括资金申请、导师建议和发现潜在合作者等。人们普遍承认,影响力大的学者往往有更多的机会获得奖项,作为他们辛勤工作的鼓励。因此,学者们在学术生涯中为取得科学成就和改善科学影响作出了巨大努力。然而,控制学者学术成功的决定性因素是什么?这个问题的答案可以帮助学者们更有效地进行研究。在此考虑下,我们的文件提出和分析对学者学术成功至关重要的因果因素。我们首先提出五个主要因素,包括以文章为中心的因素、以作者为中心的因素、以地点为中心的因素、机构中心因素和时间因素。然后,我们运用最新的先进机器学习算法和杰克尼费方法评估每个因果因素的重要性。我们的经验结果表明,以作者为中心的和以文章为中心的因素对学者今后在计算机科学领域的成功具有最高的相关性。此外,我们发现一个有趣的现象,即同一机构内每个学者的指数实际上都与同一大学内的其他指数非常接近。