基于自引网络和主路径分析的论文主题创新实证研究

[目的/意义]探讨如何从大量论文中快速发现相关论文,研究论文主题创新的方法,为研究者阅读和利用期刊论文提供参考。[方法/过程]在对论文主题创新和自引网络概念界定的基础上,提出研究论文主题创新的思路、方法和工具。以美国印第安纳大学在图书情报学领域的论文为例进行实证研究。[结果/结论]利用主路径分析可以快速从自引网络中发现由于引用而产生联系的论文,在此基础上可以进行主题创新分析。     

Google Scholar's Coverage of the Engineering Literature 10 years Later

In 2008 Meier and Conkling first tested Google Scholar's coverage of the engineering literature against citations gathered from the Compendex database. Since that time, other studies have used the same methodology and found improvement in Google Scholar's coverage. This study uses engineering dissertations from Proquest Dissertations & Theses to create a data set of citations for the comparison of fee-based databases, Compendex and Scopus, against Google Scholar. From 1950 to 2017 Google Scholar outperformed both Compendex and Scopus in discoverability of citations in nine engineering subjects. These results have implications for collection management and information literacy program planning for librarians.     

Author ranking evaluation at scale

We evaluate author impact indicators and ranking algorithms on two publication databases using large test data sets of well-established researchers. The test data consists of (1) ACM fellowship and (2) various life-time achievement awards. We also evaluate different approaches of dividing credit of papers among co-authors and analyse the impact of self-citations. Furthermore, we evaluate different graph normalisation approaches for when PageRank is computed on author citation graphs.We find that PageRank outperforms citation counts in identifying well-established researchers. This holds true when PageRank is computed on author citation graphs but also when PageRank is computed on paper graphs and paper scores are divided among co-authors. In general, the best results are obtained when co-authors receive an equal share of a paper's score, independent of which impact indicator is used to compute paper scores. The results also show that removing author self-citations improves the results of most ranking metrics. Lastly, we find that it is more important to personalise the PageRank algorithm appropriately on the paper level than deciding whether to include or exclude self-citations. However, on the author level, we find that author graph normalisation is more important than personalisation.     

The more multidisciplinary the better? – The prevalence and interdisciplinarity of research collaborations in multidisciplinary institutions

Scientific research is increasingly relying on collaborations to address complex real-world problems. Many researchers, policymakers, and administrators consider a multidisciplinary environment an important factor for fostering research collaborations, especially interdisciplinary ones that involve researchers from different disciplines. However, it remains unknown whether a higher level of multidisciplinarity within an academic institution is associated with internal collaborations that are more prevalent and more interdisciplinary. Analyzing 90,000 publications by 2500 faculty members in over 100 academic institutions from three multidisciplinary areas, information, public policy, and neuroscience, we investigated the connection between multidisciplinarity and research collaborations. Based on social network analysis and text mining, our analysis suggests that more multidisciplinary institutions are not necessarily more collaborative, although they do feature collaborations that are more interdisciplinary. Our findings provide implications for academic administrators and policymakers to promote research collaborations and interdisciplinarity in academic institutions.     

Placing Facebook

Facebook is challenging professional journalism. These challenges were evident in three incidents from 2016: the allegation that Facebook privileged progressive-leaning news on its trending feature; Facebook’s removal of the Pulitzer Prize-winning “Napalm Girl” photo from the pages of prominent users; and the proliferation of “fake news” during the US presidential election. Using theoretical concepts from the field of boundary work, this paper examines how The Guardian, The New York Times, Columbia Journalism Review and Poynter editorialized Facebook’s role in these three incidents to discursively construct the boundary between the value of professional journalism to democracy and Facebook’s ascendant role in facilitating essential democratic functions. Findings reveal that these publications attempted to define Facebook as a news organization (i.e., include it within the boundaries of journalism) so that they could then criticize the company for not following duties traditionally incumbent upon news organizations (i.e., place it outside the boundaries of journalism). This paper advances scholarship that focuses on both inward and outward conceptions of boundary work, further explores the complex challenge of defining who a journalist is in the face of rapidly changing technological norms, and advances scholarship in the field of media ethics that positions ethical analysis at the institutional level.     

关于学习控制的定量分析

本文根据著名教育技术学家瑞奇鲁斯 (C .M .Reigeluth)的影响学习控制的主要因素 ,将其进行量化处理 ,并设计出相应的计算公式 ,然后根据学习控制定量分析调查结果 ,计算学习控制的量化数据 ,利用定量分析的方法 ,指导教师制定和实施相应的教学策略。     

ASEB栅格分析法在银川市郊区旅游市场实证分析中的应用

对银川市郊区旅游市场的实证分析建立在问卷调查的基础之上,基于第一手的游客调查资料,运用ASEB栅格分析法,通过研究和实证分析,发现并解决银川市郊区旅游市场的行为特征及供给双方可能存在的问题。     

基于双曲等效的双星滑动聚束SAR两步成像算法

双星滑动聚束模式合成孔径雷达(SAR)既保持了双星SAR的灵活性、可靠性和稳定性,同时还可产生高分辨率的图像对,对于观测和测绘具有巨大的潜力.这种模式下需要兼顾双星成像与多普勒带宽显著增加的问题.分析双星滑动聚束SAR系统下目标的多普勒特性,结合双星成像算法与频谱分析法,提出一种基于双曲等效的双星滑动聚束SAR 2步成...     

学术文本中细粒度知识实体的关联分析

考察特定领域文本中蕴含的细粒度知识实体的使用情况,对知识实体的评估和选择具有重要意义。学术文本中的细粒度知识实体通常具有多个类型、多种关联关系,挖掘知识实体的同质与异质关联关系,有助于深入了解特定领域知识实体的实际使用情况。目前相关研究大多针对学术文本中单一知识实体的抽取和评估,缺乏对知识实体间关系的关注,在一定程度上限制了基于实体抽取进行知识发现的能力。文章以自然语言处理领域为例,对学术论文全文中的细粒度知识实体关联数据进行挖掘,并通过可视化方式揭示关联数据中蕴含的信息。主要是选取全国计算语言学会议2009-2018年间收录的中文论文为原始语料,人工标注论文中使用的知识实体,并针对NLP特点将其细分为“指标实体”“工具实体”“资源实体”“方法实体”4种类型;结合关联规则挖掘算法Apriori和复杂网络分析软件构建知识实体关联网络,揭示该领域常用的知识实体,以及这些知识实体的使用相关性。     

微博信息可信度分析研究综述

简述微博信息可信度研究的背景和意义,并对微博信息可信度分析等相关概念进行界定;对微博信息可信度的研究成果进行梳理,指出这些研究的不足,并认为微博信息可信度分析的关键问题是微博信息特征的提取和可信度分析方法的设计;结合微博信息的特点及已有研究成果,指出微博信息可信度分析的核心技术包括自然语言处理、社会网络分析、机器学习与数据挖掘等;最后总结全文,对微博信息可信度分析的未来发展方向进行展望,为进一步研究提供参考。