共查询到19条相似文献，搜索用时 281 毫秒
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基于WEB OF SCIENCE的理科学者H指数实证研究 总被引：1，自引：0，他引：1
通过ESI中科学家的Citations排序和CPP排序取交集选出数学、物理、化学、生物、地球科学5个理科学科代表性学者,基于Web of Science(WoS)查出这些学者的累积被引篇数P、被引次数C、篇均被引次数CPP和h指数。分析表明被引篇数P和被引次数C与h指数都有一定相关性;计算表明所有理科学者的h指数落在Hirsch公式和Egghe-Rousseau公式估计值之间,Egghe-Rousseau公式估计值、Hirsch公式估计值和真实h指数之间存在Pearson相关性。 相似文献
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p 指数运用于人才评价的有效性实证研究 总被引：2，自引：0，他引：2
h指数用于高发文、高引用的学者评价是有效的,但对低发文、高引用的学者进行评价存在缺陷,且数值易于雷同,不易区分。p指数在学者研究绩效评价方面具有同h指数相一致的维度,它不仅考虑学者的被引次数(C),而且考虑学者的研究质量指标——平均被引率(C/N)。以图书情报与文献学科领域49位专家为例,对比分析专家的发文量(N)、被引次数(C)、平均被引率、专家h指标、g指数、p指数,并进行相关性分析。结论:p指数优于现有的h指数、g指数,更具有评价的合理性,应在更大范围内进一步使用。 相似文献
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h指数与论文总被引C的幂律关系 总被引：4，自引：1，他引：3
为更深刻地理解h指数的特性,收集了学者、期刊、研究机构、大学和国家5个层面共8组h指数以及论文被引指标数据,实证研究h指数与论文总被引C之间的关系.结果表明:h指数与论文总被引C之间具有形如h=Cb、幂指数b介于0.360到0.420之间的简单幂律关系,论文总被引C的增量对于h指数的增长具有规模效应递减的规律. 相似文献
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P指数用于中文社会科学学术期刊评价的适用性分析 总被引：1，自引：0，他引：1
认为P指数在期刊绩效评价中体现了数量(被引次数C)与质量(平均被引率C/N)的平衡,应用P指数进行学术期刊评价是一种有益的探索。以法学期刊和教育学期刊为研究对象,对比分析P指数在不同学科期刊中与期刊载文量(N),被引次数(C),自被引率(SCR),5年影响因子(IF5)、期刊h指数、特征因子组合(EFS,AIS)等指标的差异,进行相关性分析,并得出以下结论:P指数简洁易计算,区分度好、支持动态变化排名、与多个关键评价指标相关性好,且在优秀学术期刊识别方面具有较好的可靠性,具有一定的现实应用前景。 相似文献
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[目的/意义]很多学科服务机构已经把学科数据的定期分析和提供学科报告作为学科服务的重要手段,但这种学科数据分析报告服务仅具有周期性,并不能及时跟踪动态变化的学科数据。本研究的目的是以h指数分析为例,对学者影响力进行动态追踪和监测,探索一种基于动态数据整合的面向预测的新型学科服务模式。[方法/过程]通过对国内和国外两个研究团队h指数发展、逐年变化趋势进行文献计量分析,获取团队成员自有成果发表年以来逐年的论文数量及其引用数据,并计算累积被引次数,逐年提取h指数。[结果/结论]团队成员中h指数增长趋势轨迹各不相同,需结合各成员任职年限、h指数增长率等数据对其在团队中的作用进行判断,论文最后对不同的h指数变化趋势在科研管理中的作用进行总结和概括。 相似文献
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从Egghe-Rousseau模型和Glanzel-Schubert模型出发,推演出h指数与总被引次数C之间的幂函数关系模型(即h-C幂律关系模型),此模型与之前的实证结果相符。模型中幂指数最大值为0.5。Hirsch模型可视为本模型的幂指数取最大值时的特殊形式。本模型应用的关键是洛特卡系数α的取值或估计,h=C~(α/(α~2+1))是一种近似简化形式。 相似文献
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We address issues concerning what one may learn from how citation instances are distributed in scientific articles. We visualize and analyze patterns of citation distributions in the full text of 350 articles published in the Journal of Informetrics. In particular, we visualize and analyze the distributions of citations in articles that are organized in a commonly seen four-section structure, namely, introduction, method, results, and conclusions (IMRC). We examine the locations of citations to the groundbreaking h-index paper by Hirsch in 2005 and how patterns associated with citation locations evolve over time. The results show that citations are highly concentrated in the first section of an article. The density of citations in the first section is about three times higher than that in subsequent sections. The distributions of citations to highly cited papers are even more uneven. 相似文献
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Despite recent evidence that Microsoft Academic is an extensive source of citation counts for journal articles, it is not known if the same is true for academic books. This paper fills this gap by comparing citations to 16,463 books from 2013 to 2016 in the Book Citation Index (BKCI) against automatically extracted citations from Microsoft Academic and Google Books in 17 fields. About 60% of the BKCI books had records in Microsoft Academic, varying by year and field. Citation counts from Microsoft Academic were 1.5 to 3.6 times higher than from BKCI in nine subject areas across all years for books indexed by both. Microsoft Academic found more citations than BKCI because it indexes more scholarly publications and combines citations to different editions and chapters. In contrast, BKCI only found more citations than Microsoft Academic for books in three fields from 2013-2014. Microsoft Academic also found more citations than Google Books in six fields for all years. Thus, Microsoft Academic may be a useful source for the impact assessment of books when comprehensive coverage is not essential. 相似文献
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Mike Thelwall 《Journal of Informetrics》2019,13(2):658-678
Articles are cited for different purposes and differentiating between reasons when counting citations may therefore give finer-grained citation count information. Although identifying and aggregating the individual reasons for each citation may be impractical, recording the number of citations that originate from different article sections might illuminate the general reasons behind a citation count (e.g., 110 citations = 10 Introduction citations + 100 Methods citations). To help investigate whether this could be a practical and universal solution, this article compares 19 million citations with DOIs from six different standard sections in 799,055 PubMed Central open access articles across 21 out of 22 fields. There are apparently non-systematic differences between fields in the most citing sections and the extent to which citations from one section overlap with citations from another, with some degree of overlap in most cases. Thus, at a science-wide level, section headings are partly unreliable indicators of citation context, even if they are more standard within individual fields. They may still be used within fields to help identify individual highly cited articles that have had one type of impact, especially methodological (Methods) or context setting (Introduction), but expert judgement is needed to validate the results. 相似文献
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《Journal of Informetrics》2014,8(1):252-258
We axiomatize the well-known Hirsch index (h-index), which evaluates researcher productivity and impact on a field, and formalize a new axiom called head-independence. Under head-independence, a decrease, to some extent, in the number of citations of “frequently cited papers” has no effect on the index. Together with symmetry and axiom D, head-independence uniquely characterizes the h-index on a certain domain of indices. Some relationships between our axiomatization and those in the literature are also investigated. 相似文献
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Antonio Quesada 《Journal of Informetrics》2009,3(2):158-160
The Hirsch index is a number that synthesizes a researcher's output. It is the maximum number h such that the researcher has h papers with at least h citations each. Woeginger [Woeginger, G. J. (2008a). An axiomatic characterization of the Hirsch-index. Mathematical Social Sciences, 56(2), 224–232; Woeginger, G. J. (2008b). A symmetry axiom for scientific impact indices. Journal of Informetrics, 2(3), 298–303] characterizes the Hirsch index when indices are assumed to be integer-valued. In this note, the Hirsch index is characterized, when indices are allowed to be real-valued, by adding to Woeginger's monotonicity two axioms in a way related to the concept of monotonicity. 相似文献
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《Journal of Informetrics》2007,1(1):2-7
We show that usually the influence on the Hirsch index of missing highly cited articles is much smaller than the number of missing articles. This statement is shown by a combinatorial argument. We further show, by using a continuous power law model, that the influence of missing articles is largest when the total number of publications is small, and non-existing when the number of publications is very large. The same conclusion can be drawn for missing citations. Hence, the h-index is resilient to missing articles and to missing citations. 相似文献
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《Journal of Informetrics》2007,1(2):170-177
Hirsch's h-index seeks to give a single number that in some sense summarizes an author's research output and its impact. Essentially, the h-index seeks to identify the most productive core of an author's output in terms of most received citations. This most productive set we refer to as the Hirsch core, or h-core. Jin's A-index relates to the average impact, as measured by the average number of citations, of this “most productive” core. In this paper, we investigate both the total productivity of the Hirsch core – what we term the size of the h-core – and the A-index using a previously proposed stochastic model for the publication/citation process, emphasising the importance of the dynamic, or time-dependent, nature of these measures. We also look at the inter-relationships between these measures. Numerical investigations suggest that the A-index is a linear function of time and of the h-index, while the size of the Hirsch core has an approximate square-law relationship with time, and hence also with the A-index and the h-index. 相似文献