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1.
《Journal of Informetrics》2020,14(3):101039
Predicting the citation counts of academic papers is of considerable significance to scientific evaluation. This study used a four-layer Back Propagation (BP) neural network model to predict the five-year citations of 49,834 papers in the library, information and documentation field indexed by the CSSCI database and published from 2000 to 2013. We extracted six paper features, two journal features, nine author features, eight reference features, and five early citation features to make the prediction. The empirical experiments showed that the performance of the BP neural network is significantly better than those of the six baseline models. In terms of the prediction effect, the accuracy of the model at predicting infrequently cited papers was higher than that for frequently cited ones. We determined that five essential features have significant effects on the prediction performance of the model, i.e., ‘citations in the first two years’, ‘first-cited age’, ‘paper length’, ‘month of publication’, and ‘self-citations of journals’, and the other features contribute only slightly to the prediction. 相似文献
2.
[目的/意义]在引文分析中,可通过论文的一些属性特征对其未来的被引情况进行预测,并通过预测结果对论文、论文作者、作者所属机构及出版物做出评价。[方法/过程] 从出版物、作者和论文三个方面对影响论文被引的多个因素展开研究,以图书馆学情报学领域被SCI索引的论文作为分析及验证数据,使用逻辑回归、GBDT、XGBoost、AdaBoost、随机森林等算法进行预测,使用多组评测指标对比不同预测方法的效果,并使用GBDT识别对论文被引影响较大的因素。[结果/结论]确定三个方面的影响因素对论文被引预测的影响程度,构建预测模型,并较好地预测论文在未来一段时间的被引情况。大量实验分析发现GBDT、XGBoost和随机森林的预测能力较强,且预测的时间段越长,效果也就相对越好。 相似文献
3.
以SJR为数据来源,比较分析了1996-2008年巴西、印度、中国、韩国4个国家发表科技论文数量、可引用文献量、文献被引量、自引量、篇均被引量、去除自引后的篇均被引量、H指数、文献引用率、国际合作量等9个指标。中国发表论文数最多,2003年后每年增加约3万篇。巴西、韩国文献引用率、篇均引用量高,且自引率低;印度居中等水平;中国文献引用率、篇均引用率低且自引率高;国际合作度巴西最高、中国最低。可见中国的科技论文质量与其他3个国家相比,还有一定的差距。 相似文献
4.
The journal impact factor (JIF) is the average of the number of citations of the papers published in a journal, calculated according to a specific formula; it is extensively used for the evaluation of research and researchers. The method assumes that all papers in a journal have the same scientific merit, which is measured by the JIF of the publishing journal. This implies that the number of citations measures scientific merits but the JIF does not evaluate each individual paper by its own number of citations. Therefore, in the comparative evaluation of two papers, the use of the JIF implies a risk of failure, which occurs when a paper in the journal with the lower JIF is compared to another with fewer citations in the journal with the higher JIF. To quantify this risk of failure, this study calculates the failure probabilities, taking advantage of the lognormal distribution of citations. In two journals whose JIFs are ten-fold different, the failure probability is low. However, in most cases when two papers are compared, the JIFs of the journals are not so different. Then, the failure probability can be close to 0.5, which is equivalent to evaluating by coin flipping. 相似文献
5.
利用各种社交网络平台,进行科学论文的获取、分享与传播已成为当前学术交流的重要形式,为此研究社交网络对科技期刊国际传播的影响。利用Elsevier数据库整合的替代计量学指标PlumX,分析PlumX与论文被引频次的关系及其学术特征。基于《中国天然药物》单刊及药理学/毒理学学科分析,均发现高被引论文的PlumX评分显著高于低被引论文,且学科高被引论文的被引频次和PlumX评分呈正相关,此外,高质量的综述和国际论文更易获得社交网络关注。研究结果发现PlumX指标能够对单篇论文的学术影响力进行快速评价,非国际权威期刊中的热点论文也可能获得较高关注,指出英文科技期刊应重视约组社交网络平台发达和应用广泛的相关主流国家优秀稿件,合理利用社交网络平台有助于获得较高的PlumX评分,进而提升发展中国家科学家的话语权。 相似文献
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��[Purpose/significance] This paper summarizes the influencing factors and prediction methods of academic paper citation, analyzes the existing problems and proposes the future development directions.[Method/process] This paper used the literature research method to review the research progress of academic papers at home and abroad, and summarized the relevant content and characteristics of influencing factors and prediction methods.[Result/conclusion] There are many indicators of influencing factors, but there is no unified selection criteria. The theoretical basis of prediction methods is weak. The research on dynamics of citation prediction is insufficient. The generality of prediction models is limited. In the future, we should strengthen the theoretical research of citation prediction methods, the combination of traditional bibliometrics and alternative metrics, the deep application of natural language processing, and establish a unified baseline standard, a more accurate prediction model. 相似文献
8.
《Journal of Informetrics》2020,14(2):101035
Performance evaluation and prediction of academic achievements is an essential task for scientists, research organizations, research funding bodies, and government agencies alike. Recently, heterogeneous networks have been used to evaluate or predict performance of multi-entities including papers, researchers, and venues with some success. However, only a minimum of effort has been made to predict the future influence of papers, researchers and venues. In this paper, we propose a new framework WMR-Rank for this purpose. Based on the dynamic and heterogeneous network of multiple entities, we extract seven types of relations among them. The framework supports useful features including the refined granularity of relevant entities such as authors and venues, time awareness for published papers and their citations, differentiating the contribution of multiple coauthors to the same paper, amongst others. By leveraging all seven types of relations and fusing the rich information in a mutually reinforcing style, we are able to predict future influence of papers, authors and venues more precisely. Using the ACL dataset, our experimental results demonstrate that the proposed approach considerably outperforms state-of-the art competitors. 相似文献
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[目的/意义] 比较分析不同学科的外文学术电子图书影响力差异,丰富电子图书评价方法,为完善电子图书分类分学科的科学评价体系提供有益参考。[方法/过程] 采用Bookmetrix,以经管类、教育类的学术电子图书为研究对象,对其传统引文指标与Altmetrics指标（Mendeley读者数、关注量、下载量）、书评量的相关性与一致性定量分析,比较两学科外文电子图书各指标之间的差异并进行非参数检验。[结果/结论] 研究发现：被引量、读者数、下载量等具有较高的指标覆盖率;经K-S Z独立双样本检验,经管类和教育类电子图书的被引量、下载量存在显著差异,关注量、读者数、书评量无显著差异（p=0.05）;指标相关性具有学科差异性,被引量与Mendeley读者数的相关性,经管类图书高于教育类图书;被引量测度的是学术电子图书的学术影响力,使用数据（下载量等）与补充计量学数据较多反映图书的社会影响力。评价中文学术电子图书应将多源异构数据处理转化,构建多指标综合评价体系,将定性与定量方法相融合,使评价更全面、科学。 相似文献
10.
《Journal of Informetrics》2022,16(1):101235
With the advancement of science and technology, the number of academic papers published each year has increased almost exponentially. While a large number of research papers highlight the prosperity of science and technology, they also give rise to some problems. As we know, academic papers are the most intuitive embodiment of the research results of scholars, which can reflect the level of researchers. It is also the standard for evaluation and decision-making of them, such as promotion and allocation of funds. Therefore, how to measure the quality of an academic paper is very critical. The most common standard for measuring the quality of academic papers is the number of citation counts of them, as this indicator is widely used in the evaluation of scientific publications. It also serves as the basis for many other indicators (such as the h-index). Therefore, it is very important to be able to accurately predict the citation counts of academic papers. To improve the effective of citation counts prediction, we try to solve the citation counts prediction problem from the perspective of information cascade prediction and take advantage of deep learning techniques. Thus, we propose an end-to-end deep learning framework (DeepCCP), consisting of graph structure representation and recurrent neural network modules. DeepCCP directly uses the citation network formed in the early stage of the paper as the input, and outputs the citation counts of the corresponding paper after a period of time. It only exploits the structure and temporal information of the citation network, and does not require other additional information. According to experiments on two real academic citation datasets, DeepCCP is shown superior to the state-of-the-art methods in terms of the accuracy of citation count prediction. 相似文献
11.
Zeynep Ceren Akyüz Ricardo Aleixo Correia 《International Information and Library Review》2017,49(1):11-18
Increasing political and financial support for scientific research in the Middle East requires academic and research communities in the region to demonstrate the visibility and impact of their scientific output. However, for countries with smaller scientific communities or lack of detailed information on their scientific production, the use of common metrics of scientific impact (e.g., number of papers, impact factor, h-index, etc.) may fail to reveal their true ability to produce high quality research, and thus guarantee the wanted societal support. In such cases, identifying and highlighting outstanding papers produced by national institutions or scientists may be another way to demonstrate scientific capacity and impact. In this context, this work aims to provide an overview of champion works (papers that have received over 1,000 citations) produced by Middle East countries. This analysis focuses on science, medicine, and technology papers featured in the Science Citation Index Expanded of Web of Science. The authors identified 213 champion works authored by Middle East scientists published since the 1970s. Israel is currently the leading nation in the Middle East in terms of published champion works, but at least one such work was identified for the majority of countries in the region. Middle East champion works were published on a diverse range of subject categories and often featured in the top journals worldwide (e.g., Science, Nature, etc.). The top institutions in the Middle East authoring champion works and their leading collaborating countries worldwide are listed, and the role of international scientific collaborations in achieving these highly cited papers is highlighted. 相似文献
12.
《Journal of Informetrics》2023,17(1):101379
We performed a citation analysis on the Web of Science publications consisting of more than 63 million articles and over a billion citations on 254 subjects from 1981 to 2020. We proposed the Article’s Scientific Prestige (ASP) metric and compared this metric to number of citations (#Cit) and journal grade in measuring the scientific impact of individual articles in the large-scale hierarchical and multi-disciplined citation network. In contrast to #Cit, ASP, that is computed based on the eigenvector centrality, considers both direct and indirect citations, and provides steady-state evaluation cross different disciplines. We found that ASP and #Cit are not aligned for most articles, with a growing mismatch amongst the less cited articles. While both metrics are reliable for evaluating the prestige of articles such as Nobel Prize winning articles, ASP tends to provide more persuasive rankings than #Cit when the articles are not highly cited. The journal grade, that is eventually determined by a few highly cited articles, is unable to properly reflect the scientific impact of individual articles. The number of references and coauthors are less relevant to scientific impact, but subjects do make a difference. 相似文献
13.
Qing Ke 《Journal of Informetrics》2018,12(3):706-717
Traditionally, the number of citations that a scholarly paper receives from other papers is used as the proxy of its scientific impact. Yet citations can come from domains outside the scientific community, and one such example is through patented technologies—paper can be cited by patents, achieving technological impact. While the scientific impact of papers has been extensively studied, the technological aspect remains less known in the literature. Here we aim to fill this gap by presenting a comparative study on how 919 thousand biomedical papers are cited by U.S. patents and by other papers over time. We observe a positive correlation between citations from patents and from papers, but there is little overlap between the two domains in either the most cited papers, or papers with the most delayed recognition. We also find that the two types of citations exhibit distinct temporal variations, with patent citations lagging behind paper citations for a median of 6 years for the majority of papers. Our work contributes to the understanding of the technological impact of papers. 相似文献
14.
Adrian Miroiu 《Journal of Informetrics》2013,7(1):10-15
Scientific impact indexes like h are responsive to two parameters: the researcher's productivity given by the number of her published papers (an aspect of quantity) and citations (an aspect of quality). In this paper I prove that the two parameters can be treated separately: the index h can be axiomatized by appealing (1) only to axioms that allow for productivity changes, but do not require taking into account distinct situations in which a researcher's papers received different numbers of citations or (2) only to axioms that allow for changes in the number of citations received by the researcher's papers, but do not require changes in scientific productivity. The axioms used are weak. Specifically, monotonicity is avoided. 相似文献
15.
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
16.
《Journal of Informetrics》2022,16(2):101283
Choosing a publication venue for an academic paper is a crucial step in the research process. However, in many cases, decisions are based solely on the experience of researchers, which often leads to suboptimal results. Although there exist venue recommender systems for academic papers, they recommend venues where the paper is expected to be published. In this study, we aim to recommend publication venues from a different perspective. We estimate the number of citations a paper will receive if the paper is published in each venue and recommend the venue where the paper has the most potential impact. However, there are two challenges to this task. First, a paper is published in only one venue, and thus, we cannot observe the number of citations the paper would receive if the paper were published in another venue. Secondly, the contents of a paper and the publication venue are not statistically independent; that is, there exist selection biases in choosing publication venues. In this paper, we formulate the venue recommendation problem as a treatment effect estimation problem. We use a bias correction method to estimate the potential impact of choosing a publication venue effectively and to recommend venues based on the potential impact of papers in each venue. We highlight the effectiveness of our method using paper data from computer science conferences. 相似文献
17.
《Journal of Informetrics》2019,13(4):100977
Main path analysis is a popular method for extracting the backbone of scientific evolution from a (paper) citation network. The first and core step of main path analysis, called search path counting, is to weight citation arcs by the number of scientific influence paths from old to new papers. Search path counting shows high potential in scientific impact evaluation due to its semantic similarity to the meaning of scientific impact indicator, i.e. how many papers are influenced to what extent. In addition, the algorithmic idea of search path counting also resembles many known indirect citation impact indicators. Inspired by the above observations, this paper presents the FSPC (Forward Search Path Count) framework as an alternative scientific impact indicator based on indirect citations. Two critical assumptions are made to ensure the effectiveness of FSPC. First, knowledge decay is introduced to weight scientific influence paths in decreasing order of length. Second, path capping is introduced to mimic human literature search and citing behavior. By experiments on two well-studied datasets against two carefully created gold standard sets of papers, we have demonstrated that FSPC is able to achieve surprisingly good performance in not only recognizing high-impact papers but also identifying undercited papers. 相似文献
18.
睡美人与王子文献的识别方法研究 总被引：1，自引：0，他引：1
[目的/意义] 研究睡美人与王子文献的识别方法。分析唤醒机制,为未来在学术交流体系中发现"王子"作者,发掘、唤醒低被引和零被引文献的潜在价值提供理论依据。[方法/过程] 采用被引速率指标和睡美人指数两种客观指标识别1970-2005年临床医学四大名刊上发表的睡美人文献;基于以下4个原则寻找唤醒睡美人的王子文献:①发表于被引突增的附近年份;②本身被引次数较高;③与睡美人文献的同被引次数高;④在年度被引次数曲线上,王子文献对睡美人文献的"牵引或拉动"作用非常显著,即至少在睡美人文献引用突增的附近年份,王子文献的年度被引次数应高于睡美人文献。[结果/结论] 由于考虑了全部引文窗的引文曲线,被引速率指标能够识别出那些被引生命周期长、至今仍持续不断高频被引的论文;睡美人指数能够快速识别出睡美人文献,但却无法反映年度被引次数达到峰值之后的引文曲线;将被引速率+发表最初5年年均被引次数两个指标结合起来能够更好地识别睡美人文献。分析发现,综述、指南、著作等"共识型"的文献对于引发那些提出了新思想但尚未被认可的睡美人文献的被引突增起到了关键作用。建议事后识别睡美人文献可采用客观指标与主观界定相结合的方法,事前预测睡美人文献要注意追踪其是否被"共识性"文献推荐和引用,学术评价要特别关注被引速率低的论文。 相似文献
19.
《Journal of Informetrics》2022,16(4):101333
The number of clinical citations received from clinical guidelines or clinical trials has been considered as one of the most appropriate indicators for quantifying the clinical impact of biomedical papers. Therefore, the early prediction of clinical citation count of biomedical papers is critical to scientific activities in biomedicine, such as research evaluation, resource allocation, and clinical translation. In this study, we designed a four-layer multilayer perceptron neural network (MPNN) model to predict the clinical citation count of biomedical papers in the future by using 9,822,620 biomedical papers published from 1985 to 2005. We extracted ninety-one paper features from three dimensions as the input of the model, including twenty-one features in the paper dimension, thirty-five in the reference dimension, and thirty-five in the citing paper dimension. In each dimension, the features can be classified into three categories, i.e., the citation-related features, the clinical translation-related features, and the topic-related features. Besides, in the paper dimension, we also considered the features that have previously been demonstrated to be related to the citation counts of research papers. The results showed that the proposed MPNN model outperformed the other five baseline models, and the features in the reference dimension were the most important. In all the three dimensions, the citation-related and topic-related features were more important than the clinical translation-related features for the prediction. It also turned out that the features helpful in predicting the citation count of papers are not important for predicting the clinical citation count of biomedical papers. Furthermore, we explored the MPNN model based on different categories of biomedical papers. The results showed that the clinical translation-related features were more important for the prediction of clinical citation count of basic papers rather than those papers closer to clinical science. This study provided a novel dimension (i.e., the reference dimension) for the research community and could be applied to other related research tasks, such as the research assessment for translational programs. In addition, the findings in this study could be useful for biomedical authors (especially for those in basic science) to get more attention from clinical research. 相似文献
20.
对h指数的研究和探索已经成为科学计量学和科技评价研究的前沿领域之一,但是单纯的数学研究方法可能因更多关注数量方面（包括被引次数与被引论文数）的直接比较,而忽视其内在价值的变化。文章引入价值理论对h指数解读和分析,并在此基础上对目前流行的h型指数进行了比较。 相似文献