特征因子原理及实证研究

介绍了JCR新引入的期刊评价指标——特征因子的工作原理和计算方法,并用软件实现了该算法。以18种CSSCI收录图书情报学期刊为例,计算出它们的特征因子分值和论文影响分值,在此基础上对这两项指标同其他期刊评价指标的关系进行了探讨,结果表明,特征因子分值、论文影响分值和期刊综合指数、h指数、影响因子之间存在较强的皮尔逊相关性。     

特征因子与论文影响力指标初探

针对汤森路透近日推出的量化学术评价方法——特征因子计量方法及其指标特征因子值和论文影响值的原理、优势、不足,进行理论和量化分析,指出特征因子计量方法的潜力和利用时所需注意的问题,以期为特征因子计量方法的研究和利用提供科学的依据。     

基于Twitter关注度的期刊影响力评价指标——以国际图书情报学顶级期刊为例

[目的/意义] 随着社交媒体和电子出版平台的兴起,利用期刊在Twitter上的关注度来评价期刊能够对传统的期刊评价方式进行补充,发现该指标与传统指标之间的相关性关系,并以期最终构建合理的期刊社交网络影响力评价指标。[方法/过程] 根据《期刊引用报告》(Journal Citation Reports,JCR)的社会科学版,选取国际图书情报学领域影响因子前30位的期刊作为该领域的国际顶级期刊。为了研究altmetrics指标与传统的基于引文的评价指标间的相关关系,利用Spearman非参数相关性分析对期刊Twitter提及频次与8个传统指标(总被引数、影响因子、5年期影响因子、即年指标、论文数、引文半衰期、特征因子和论文影响分值)之间的相关性进行分析。[结果/结论] 统计结果显示,JASIST、College & Research Libraries和Scientometrics是本领域中在Twitter上受关注度最高的期刊。期刊Twitter提及频次仅与期刊特征因子间存在中等的显著相关性,与其他指标间存在较弱的相关性。值得注意的是,相比其他期刊,在Twitter上设有官方账号的期刊明显得到更高的关注度。     

学术社交网络中科研成果浏览行为规律及影响因素分析

[目的/意义]探究学术社交网络中科研成果浏览行为规律及其影响因素,有助于用户基于学术社交网络提升科研成果的传播效果和影响力。[方法/过程]文章选取ResearchGate中32501篇科研成果,揭示其浏览量整体分布和以周为单位的时序变化规律。从第一作者、合作者数量、展现形式以及质量水平四个维度利用相关分析,并结合主成分分析改进和逻辑回归分析对科研成果浏览量总量影响因素进行探究。[结果/结论]研究发现学术社交网络中科研成果总浏览量呈现幂律分布特征;以周为观察周期,多数科研成果的传播效果仍较小;科研成果的浏览量时序变化可细分为骤升骤降型、持续升降型、平稳变化型、低浏览量型四种类型;反映科研成果在RG上呈现内容完整度的全文信息和图片信息、第一作者的篇均浏览量、反映科研成果质量的科研成果兴趣值和引用量均显著正向影响科研成果的浏览量水平,且影响程度依次降低;丰富科研成果展现形式与内容,提升完整成果能见度是学术社交网络平台及其用户提升科研成果浏览量水平的关键。     

特征因子与影响因子及总被引频次的指标特性比较 ——基于“TOP期刊群”样本的实例分析

通过特征因子与影响因子及总被引频次的指标比较分析和实例佐证,发现特征因子在跨学科评价方面的优势较突出。对我国的优秀期刊进行特征因子指标分析,得到了与常规影响因子不同的结论:1)我国一流期刊实际的学术影响力远低于国际一流期刊的影响力;2)相对于其他学科,我国物理和化学学科期刊在国际上的学术影响力较大,在影响因子排名处于劣势的我国数学类期刊,在国际上也具有一定的影响力。     

Information Sources and Indicators for the Assessment of Journal Reputation and Impact

This guide describes several information sources that can be used to assist faculty interested in quantitative and qualitative assessments of journal reputation and scholarly impact: Journal Citation Reports, Eigenfactor, Google Scholar Metrics, Elsevier Journal Metrics, Excellence in Research for Australia, Cabell’s International, Web of Science, Scopus, Google Scholar, and Beall’s List. It also introduces the indicators most often used to represent citation impact: impact factor, article influence score, eigenfactor, h5-index, source normalized impact per paper, impact per publication, and SCImago journal rank. Methods of assessing the influence of individual articles are also presented, along with strategies for the identification of predatory or low-quality journals.     

改进Z指数的跨学科期刊评价

针对Z指数不能实现跨学科领域期刊评价的缺陷,文章通过引入学科规范化引文影响力修正不同学科的引用差异,改进Z指数并提出ZCNCI指数。通过分析ZCNCI指数与Z指数、P指数、SNIP、标准化特征因子、影响因子百分位等指标的相关性和差异性,验证ZCNCI指数跨学科期刊评价的效力。结果表明,ZCNCI指数延续了Z指数综合反映期刊数量、质量和被引分布特征的优势,与Z指数、P指数、标准化特征因子的相关性较高,且克服了SNIP、影响因子百分位的评价缺陷,在跨学科期刊评价中的综合表现较好。ZCNCI指数具有跨学科期刊评价效力,可用于跨学科期刊评价。     

引用质量加权影响因子评价学术期刊研究-以CSSCI图书情报学期刊评价为例

构建一种考虑引用质量的加权影响因子:IFwh指数,该指数既能反映影响因子与H指数计算简便、设计巧妙的优点,又体现SJR指数与Eigenfactor计算算法中考虑引用质量的思想,以期客观科学地反映期刊的影响力。在详细阐述IFwh指数设计思想的基础上,以CSSCI收录的我国图书情报学期刊为例进行实证研究,一方面检验利用IFwh指数评价学术期刊的可行性;另一方面通过分析IFwh指数评价与影响因子、H指数评价以及CSSCI评价的关系,研究IFwh指数的特性。IFwh指数同样适用于学科专业和科教人员等层面的学术影响力评价。     

Data,disciplines, and scholarly publishing

Data are becoming an essential product of scholarship, complementing the roles of journal articles, papers, and books. Research data can be reused to ask new questions, to replicate studies, and to verify research findings. Data become even more valuable when linked to publications and other related resources to form a value chain. Types and uses of data vary widely between disciplines, as do the online availability of publications and the incentives of scholars to publish their data. Publishers, scholars, and librarians each have roles to play in constructing a new scholarly information infrastructure for e‐research. Technical, policy, and institutional components are maturing; the next steps are to integrate them into a coherent whole. Achieving a critical mass of datasets in public repositories, with links to and from publisher databases, is the most promising solution to maintaining and sustaining the scholarly record in digital form.     

科技期刊影响力数据稳定性的数学描述

参照统计学中的置信限的概念,定义了影响力数据的置信限和稳定度。这一定义不仅综合考虑了影响因子、特征因子、总被引频次、载文量等文献计量指标,而且具有计算简便、可操作性较强的特点。通过对工程技术类79种期刊的实例计算与分析,进一步论证了有关影响因子的置信限与稳定度在研究评价和期刊评估中的实用性。     

人文社会科学期刊特征因子评价研究——基于CSSCI法学期刊

以特征因子计量方法对CSSCI收录的21种法学期刊进行统计分析。通过与影响因子计量方法的深入比较,指出特征因子计量方法的优势与不足,并对特征因子计量方法的使用提出建议,以增强期刊评价的合理性。     

Does the public discuss other topics on climate change than researchers? A comparison of explorative networks based on author keywords and hashtags

Twitter accounts have already been used in many scientometric studies, but the meaningfulness of the data for societal impact measurements in research evaluation has been questioned. Earlier research focused on social media counts and neglected the interactive nature of the data. We explore a new network approach based on Twitter data in which we compare author keywords to hashtags as indicators of topics. We analyze the topics of tweeted publications and compare them with the topics of all publications (tweeted and not tweeted). Our exploratory study is based on a comprehensive publication set of climate change research. We are interested in whether Twitter data are able to reveal topics of public discussions which can be separated from research-focused topics. We find that the most tweeted topics regarding climate change research focus on the consequences of climate change for humans. Twitter users are interested in climate change publications which forecast effects of a changing climate on the environment and to adaptation, mitigation and management issues rather than in the methodology of climate-change research and causes of climate change. Our results indicate that publications using scientific jargon are less likely to be tweeted than publications using more general keywords. Twitter networks seem to be able to visualize public discussions about specific topics.     

基于隐喻思维的引文分析研究与创新

[目的/意义]对引文分析领域的研究与创新进行回顾和展望。[方法/过程]运用隐喻思维方法,以"科学睡美人"和第二代期刊评价指标（EF和SJR）的创设为例,对引文分析领域取得的主要研究进展进行说明;同时,通过对生态学相关研究文献的调研分析,提出将生态学原理引入到未来引文分析研究中来,形成一个新的研究方向——引文生态学,并就若干重要研究议题进行简要说明。[结果/结论]隐喻思维方法在学术研究及方法论创新中具有重要作用,引文生态学的提出对引文分析领域的理论完善和应用创新具有重要价值。     

Research assessment by percentile-based double rank analysis

In the double rank analysis of research publications, the local rank position of a country or institution publication is expressed as a function of the world rank position. Excluding some highly or lowly cited publications, the double rank plot fits well with a power law, which can be explained because citations for local and world publications follow lognormal distributions. We report here that the distribution of the number of country or institution publications in world percentiles is a double rank distribution that can be fitted to a power law. Only the data points in high percentiles deviate from it when the local and world μ parameters of the lognormal distributions are very different. The likelihood of publishing very highly cited papers can be calculated from the power law that can be fitted either to the upper tail of the citation distribution or to the percentile-based double rank distribution. The great advantage of the latter method is that it has universal application, because it is based on all publications and not just on highly cited publications. Furthermore, this method extends the application of the well-established percentile approach to very low percentiles where breakthroughs are reported but paper counts cannot be performed.     

Does a gender-neutral name associate with the research impact of a scientist?

Women have long suffered in both academia and industry from lower status, underrepresentation, unequal treatment, and other challenges based on gender. This paper explores whether a gender-neutral name is associated with the research impact of a scientist by examining three years of citations. The data for this study are derived from publications indexed in ISI's Web of Science (WoS) database from 2009 to 2015. By assigning a name neutralization index score to each author, we investigate the relationship between the neutralization index and citations and find the following results: (1) generally, the more neutral the name, the more citations the publications received; (2) the neutral effect was more pronounced for authors with feminine names than for authors with masculine names.     

Research universities on the ResearchGate social networking site: An examination of institutional differences,research activity level,and social networks formed

As one of the largest active academic social networking sites, ResearchGate (RG) has been utilized by scholars to share publications, seek collaborators, communicate work in progress, and build scholarly reputation. This study collects data from RG users from 61 U.S. research universities at different research activity levels, as categorized by the Carnegie Classification of Institutions of Higher Education, to examine the impact of institutional differences on RG reputational metrics. The results confirm that RG is a research-oriented academic social networking site that closely and realistically mirrors the research activity level of institutions. With an increase in the research activity level of a university, its affiliated RG users tend to have higher RG scores, more publications and citations, and more profile views and followers, while the average number of reads of their publications and followees tend to be lower and fluctuant. In addition, RG users primarily follow others from institutions of a higher research activity level, forming virtual social networks centered around esteemed institutions. The study suggests academic social networks can serve as indicators in evaluation of research activities among research institutions, and such sites can be helpful and credible for acquiring resources, keeping informed about research, and promoting academic influence.     

On the relation between the degree of internationalization of cited and citing publications: A field level analysis,including and excluding self-citations

The growing complexity of scientific challenges demands increasingly intense research collaboration, both domestic and international. The resulting trend affects not only the modes of producing new knowledge, but also the way it is disseminated within scientific communities. This paper analyses the relationship between the “degree of internationalization” of a country’s scientific production and that of the relevant citing publications. The empirical analysis is based on 2010-2012 Italian publications. Findings show: i) the probability of being cited increases with the degree of internationalization of the research team; ii) totally domestic research teams tend to cite to a greater extent totally domestic publications; iii) vice versa, publications resulting from international collaborations tend to be more cited by totally foreign publications rather than by publications including domestic authors. These results emerge both at overall and at discipline level. Findings might inform research policies geared towards internationalization.     

我国国际论文的真实影响力分析

科学研究的目的在于创造知识,并应用理论成果解决我国社会、经济、文化等发展中的实际问题。将论文发表在国际期刊上可以让更多的国际同行了解我国最新的科研成果,为我国获得更多的国际影响力,所以在过去二十多年里SCI论文成为我国科研考核的一个重要指标。在这种科研评价导向下,我国学者发表的国际论文数量已居世界第一位,而大量来自国内同行的引用使得我国国际论文的被引量排名世界第二。本文提取1990至2015年Web of Science论文及其引文的数据,分析不同国家、不同学科在国家层次的自引情况,并在不同国家、不同学科之间进行比较。研究发现,在排除国内同行的自引后,我国国际论文的真实国际影响力仍然有限,除了临床医学和物理等少数学科外,其他学科仍然低于全球平均水平。     

On the relationship between download and citation counts: An introduction of Granger-causality inference

Studies on the relationship between the numbers of citations and downloads of scientific publications is beneficial for understanding the mechanism of citation patterns and research evaluation. However, seldom studies have considered directionality issues between downloads and citations or adopted a case-by-case time lag length between the download and citation time series of each individual publication. In this paper, we introduce the Granger-causal inference strategy to study the directionality between downloads and citations and set up the length of time lag between the time series for each case. By researching the publications on the Lancet, we find that publications have various directionality patterns, but highly cited publications tend to feature greater possibilities to have Granger causality. We apply a step-by-step manner to introduce the Granger-causal inference method to information science as four steps, namely conducting stationarity tests, determining time lag between time series, establishing cointegration test, and implementing Granger-causality inference. We hope that this method can be applied by future information scientists in their own research contexts.