共查询到19条相似文献，搜索用时 109 毫秒
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参考文献的引用及影响引用的因素分析 总被引：6，自引：2，他引：4
分析参考文献的功能、作用、来源及其引用动机等。希望论文作者和编辑能对文献有全面科学的认识,并能合理地引用,也希望编辑界有识之士对文献著录原则和规范等问题进行学术研究和指导,以促进文献引用的进一步规范化,使文献能为我国科技论文整体水平的提高发挥其应有的作用。 相似文献
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参考文献引用错误是科技论文的严重缺陷 总被引：1，自引：0，他引：1
参考文献是科技论文的一个重要组成部分,文章主要介绍了在日常编辑工作中常见的引用参考文献易出现的5类错误:缺乏引用密切相关的重要文献,所列引文文献是作者未真正查阅过的,引用了有时效性的无效文献,引用内容错误或不当的文献,参考文献引用格式上的错误。本文对这5类常见的引用错误进行了分析和讨论,希望能引起作者及编辑的足够重视,严格把关,减少甚至杜绝参考文献的引用错误,以利于作者、编者共同努力办好学术期刊。 相似文献
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针对科技论文中参考文献引用的科学性差错,提出从参考文献引用的必要性、准确性2方面进行审核的方法.结合实例对这2个方面共6种常见错误进行剖析并给出了修改方法:遗漏必要文献、堆砌非必要文献、非对应引用文献、错误引用文献、引用错误文献、引用陈旧文献.分析了参考文献引用出现科学性差错的主客观原因,提出了期刊编辑和审稿人在工作中... 相似文献
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参考文献引用分类标注与科技期刊和论文的评价 总被引：6，自引：0，他引：6
为了规范参考文献的引用,减小引文问题对科技期刊和论文评价所造成的影响,使得以引文为基础的各种分析方法在科技期刊和论文的评价中更加合理、更加科学,提出了参考文献引用分类标注的概念.依据被引文献对论文作用的不同和引用意义的差别,将论文所引用参考文献分为正相关性引用类、负相关性引用类和平行相关性引用类,建议在论文写作中标注出分类的标志,编辑和审稿人在编审时可据此核实参考文献引用的正确性.在论文评价时分类进行检索统计,有利于进一步规范引文分析的各项指标,变笼统的引文分析为细化的据类分析评价,使引文分析评价体系更加科学、更加可靠. 相似文献
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应注重参考文献引用的学术论证功能 总被引：10，自引：0，他引：10
创新性、科学性和应用性是科技论文的基本特性;参考文献引用的基本目的和核心作用是对科技论文的创新性、科学性、应用价值等进行学术论证;而其他多方面的功能和具体作用(例如学术归誉、学术评价、文献检索、文献计量研究、著作权保护、节约篇幅,等等)都是由"学术论证"这一最基本的功能衍生而来。在科技论文写作与编辑中应注重参考文献引用的学术论证功能。 相似文献
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应重视科技论文文献的引用质量 总被引：1，自引：0，他引：1
科技论文文献的引用质量是指文献的引用是否真正起到对论文的支撑作用。当前科技论文文献引用质量存在以下问题:引用不权威;引用不全面;引用不合理。针对以上问题,提出了提高科技论文文献引用质量的措施。 相似文献
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分析了学术期刊中参考文献超前引用的原因、特点和弊端,指出了学术期刊编辑对此应采取的一系列措施,具体包括:论文责任编辑审稿前要求作者提供被超前引用的文献,核红时尽量补全被超前引用文献的著录项,连续刊载作者的系列研究论文,开辟系列研究专栏。 相似文献
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通过对《第三军医大学学报》近2 年的作者行问卷调查和访谈,收集检索、阅读、引用医学文献等数据并作统计学分析.研究结果显示: 近2 年医科大学学报博士以上研究论文类优质稿件大量流失; 以硕士研究生为绝对主体的作者群带来英文文献引用增多和学报自身的有效阅读量减少; CNKI 与 PubMed 分别为医科大学学报作者群检索中、英文文献的最常用数据库.提出医科大学学报编辑部应重视论文发表的后续服务,编辑应将工作重点放在提升论文检出率等建议. 相似文献
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基于引用内容的论文影响力研究——以诺贝尔奖获得者论文为例 总被引：1，自引：0，他引：1
[目的/意义]论文被引频次只能反映论文的宏观影响力,无法揭示论文在他人研究中的具体作用和影响,因此,本文提出从引用内容的主题和功能两方面对论文的影响力进行分析。[方法/过程]以2014年诺贝尔生理学或医学奖获得者J.O'Keefe的高被引论文为实例,首先,采用文献计量学方法对引用内容主题进行分析;对其,影响范围及领域进行可视化分析;其次,从引用性质和功能角度,将引用内容分成正面引用、负面引用和中性引用;最后,将中性引用进一步划分为3类,分别是研究背景介绍、理论基础和实验基础。[结果/结论]结果表明,共词分析可以很好地表达论文影响的主题领域;引用内容的分类可以提供一篇论文被引用的多方面原因。在本实验中没有负面引用,多于10%的引用为正面引用,大约50%的中性引用都是作者在研究背景章节中介绍与施引文献相关的研究工作。 相似文献
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孙安 《大学图书情报学刊》2009,27(3):57-60
文章对我国四大引文数据库及其期刊引证报告的发展和现状做了简要总结。对这四大引文数据库极其引证报告的特色进行了比较,指出了四大引文数据库及其引证报告的不足,并提出了改进的建议与措施。 相似文献
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Citation numbers are extensively used for assessing the quality of scientific research. The use of raw citation counts is generally misleading, especially when applied to cross-disciplinary comparisons, since the average number of citations received is strongly dependent on the scientific discipline of reference of the paper. Measuring and eliminating biases in citation patterns is crucial for a fair use of citation numbers. Several numerical indicators have been introduced with this aim, but so far a specific statistical test for estimating the fairness of these numerical indicators has not been developed. Here we present a statistical method aimed at estimating the effectiveness of numerical indicators in the suppression of citation biases. The method is simple to implement and can be easily generalized to various scenarios. As a practical example we test, in a controlled case, the fairness of fractional citation count, which has been recently proposed as a tool for cross-discipline comparison. We show that this indicator is not able to remove biases in citation patterns and performs much worse than the rescaling of citation counts with average values. 相似文献
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引文主题与源文献主题具有相关性.基于这一事实,本文提出了一种基于汉语科技文献引文的自动标引法.具体过程是以词典为依据对源文献与引文题名进行抽词处理,并为不同类型引文赋予不同的权重,在此基础上确定用于标引源文献的关键词.本文还对此法的可行性进行了测试,并提出了改进方法. 相似文献
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《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
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《Journal of Informetrics》2020,14(2):101019
The normalized citation indicator may not be sufficiently reliable when a short citation time window is used, because the citation counts for recently published papers are not as reliable as those for papers published many years ago. In a limited time period, recent publications usually have insufficient time to accumulate citations and the citation counts of these publications are not sufficiently reliable to be used in the citation impact indicators. However, normalization methods themselves cannot solve this problem. To solve this problem, we introduce a weighting factor to the commonly used normalization indicator Category Normalized Citation Impact (CNCI) at the paper level. The weighting factor, which is calculated as the correlation coefficient between citation counts of papers in the given short citation window and those in the fixed long citation window, reflects the degree of reliability of the CNCI value of one paper. To verify the effect of the proposed weighted CNCI indicator, we compared the CNCI score and CNCI ranking of 500 universities before and after introducing the weighting factor. The results showed that although there was a strong positive correlation before and after the introduction of the weighting factor, some universities’ performance and rankings changed dramatically. 相似文献
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Gui-Yuan Shi Yi-Xiu Kong Guang-Hui Yuan Rui-Jie Wu An Zeng Matúš Medo 《Journal of Informetrics》2019,13(2):717-725
Identifying the future influential papers among the newly published ones is an important yet challenging issue in bibliometrics. As newly published papers have no or limited citation history, linear extrapolation of their citation counts—which is motivated by the well-known preferential attachment mechanism—is not applicable. We translate the recently introduced notion of discoverers to the citation network setting, and show that there are authors who frequently cite recent papers that become highly-cited in the future; these authors are referred to as discoverers. We develop a method for early identification of highly-cited papers based on the early citations from discoverers. The results show that the identified discoverers have a consistent citing pattern over time, and the early citations from them can be used as a valuable indicator to predict the future citation counts of a paper. The discoverers themselves are potential future outstanding researchers as they receive more citations than average. 相似文献