Can Google Scholar and Mendeley help to assess the scholarly impacts of dissertations?

Dissertations can be the single most important scholarly outputs of junior researchers. Whilst sets of journal articles are often evaluated with the help of citation counts from the Web of Science or Scopus, these do not index dissertations and so their impact is hard to assess. In response, this article introduces a new multistage method to extract Google Scholar citation counts for large collections of dissertations from repositories indexed by Google. The method was used to extract Google Scholar citation counts for 77,884 American doctoral dissertations from 2013 to 2017 via ProQuest, with a precision of over 95%. Some ProQuest dissertations that were dual indexed with other repositories could not be retrieved with ProQuest-specific searches but could be found with Google Scholar searches of the other repositories. The Google Scholar citation counts were then compared with Mendeley reader counts, a known source of scholarly-like impact data. A fifth of the dissertations had at least one citation recorded in Google Scholar and slightly fewer had at least one Mendeley reader. Based on numerical comparisons, the Mendeley reader counts seem to be more useful for impact assessment purposes for dissertations that are less than two years old, whilst Google Scholar citations are more useful for older dissertations, especially in social sciences, arts and humanities. Google Scholar citation counts may reflect a more scholarly type of impact than that of Mendeley reader counts because dissertations attract a substantial minority of their citations from other dissertations. In summary, the new method now makes it possible for research funders, institutions and others to systematically evaluate the impact of dissertations, although additional Google Scholar queries for other online repositories are needed to ensure comprehensive coverage.     

Predicting citation counts based on deep neural network learning techniques

With the growing number of published scientific papers world-wide, the need to evaluation and quality assessment methods for research papers is increasing. Scientific fields such as scientometrics, informetrics, and bibliometrics establish quantified analysis methods and measurements for evaluating scientific papers. In this area, an important problem is to predict the future influence of a published paper. Particularly, early discrimination between influential papers and insignificant papers may find important applications. In this regard, one of the most important metrics is the number of citations to the paper, since this metric is widely utilized in the evaluation of scientific publications and moreover, it serves as the basis for many other metrics such as h-index. In this paper, we propose a novel method for predicting long-term citations of a paper based on the number of its citations in the first few years after publication. In order to train a citation count prediction model, we employed artificial neural network which is a powerful machine learning tool with recently growing applications in many domains including image and text processing. The empirical experiments show that our proposed method outperforms state-of-the-art methods with respect to the prediction accuracy in both yearly and total prediction of the number of citations.     

专利权人引用网络中的媒介角色研究

对专利权人引用网络中的3种类型的媒介角色的概念、算法等进行梳理,根据媒介角色理论和知识交流理论对媒介角色是否影响专利权人技术创新能力做理论推导,并提出3个假设。通过实证分析发现,不同的角色影响程度各不相同:中介中心性对专利权人技术创新能力的影响力最大;5种桥系数小于中介中心性,影响最小的是结构洞约束。     

引文分析领域研究热点前沿与高频作者的二维时空分析

以国内引文分析领域的3401篇论文作为样本,统计其30年间的年代分布状况;分析该领域的高频作者;借助CiteSpaceⅡ突变检测算法检测到引文分析领域的研究热点及前沿术语。运用CiteSpaceⅡ提供的复杂混合网络的可视化方法,将研究热点、研究前沿与作者建立空间上和时间上的对应关系,从空间和时间两个维度揭示我国引文分析领域研究热点的作者分布,研究前沿的作者分布以及引领引文分析研究前沿的关键作者。     

评析工具书对图书馆、情报与文献学科学术发展的影响

本文借助《中文社会科学引文索引》(2000-2007)数据,利用引文分析法,对图书馆、情报与文献学学科论文引用的图书进行了统计,推出该学科领域最具学术影响力的21种工具书。通过简要介绍这些著作的内容,并结合来源文献主题对这些著作被引情况进行分析,以反映这些著作在本领域内的学术影响。     

网络分析方法在引文分析中的整合研究

随着社会网络分析在各学科领域的不断渗透,复杂网络理论的兴起和可视化技术的进步,引文分析面临着研究范式从个体主义到整体主义的转变,研究方法从统计分析到网络分析的融合。本文通过对引文网络形成过程和本质属性的探究,以及对网络分析方法理论前提和分析原理的考察,探索网络分析方法在引文分析中的整合途径。具体包括测度引文网络整体结构特性,探究网络结构的形成机制和演化过程,分析引文网络的细分结构、可视化引文分析过程与结果等方面的整合。图1。表2。参考文献28。     

使用引文分析法考察图书馆文献保障情况

博士生在撰写学位论文的过程中,需要收集大量的文献资料,而图书馆是他们获取文献的主要渠道。对博士论文所附的参考文献进行引文分析,可以了解图书馆的文献保障情况。该文以北京大学物理学院理论物理专业2003-2009年的42篇博士论文为调查对象,对引文量、文献类型、引文发表时间、引文语种等情况进行统计分析,讨论了被引量最高的外文期刊的保障情况,同时还探讨了网络OA资源对图书馆资源建设的影响。     

应用于钱塘江、太湖流域面雨量预报的MM5产品释用技术

在2007～2009年浙江省气象台钱塘江、太湖流域面雨量24 h、48 h预报的基础之上,开展了面雨量释用研究,采用的方法为对数线性释用方法,释用后预报可信度明显增加.本文对面雨量的模糊评分方法做了改进,使之更具有实际可操作性.试验证明,用绝对误差和改进的模糊评分两种办法结合起来对面雨量预报做检验,能够较客观的反映预报...     

基于遗传算法参数优化的最小二乘支持向量机财务困境预测

传统支持向量机应用于财务困境预测时,需要求解复杂的二次规划问题,求解难度大。而最小二乘支持向量机模型可以将二次规划问题变成一个线性方程组来求解,有效降低了模型求解的难度。尤其是将遗传算法应用于最小二乘支持向量机模型参数和核参数的优化时,显著提高了模型预测的正确率。本文从沪深两市随机抽取了2002年-2007年252家A股上市公司作为研究样本,并把研究样本分为两组,对这两组样本数据分别进行了短期及中长期预测。实证结果表明,基于遗传算法的最小二乘支持向量机模型的预测效果不但好于传统统计类Logit模型,也优于传统支持向量机模型。短期预测效果显著优于中长期预测效果,训练样本数直接影响到模型的预测效果,二者呈正相关关系。