基于灰色系统的各学科SCI影响因子模型研究

发布时间：2018-09-03 07:50

【摘要】：影响因子在引文分析中扮演着重要的角色,是JCR所提供的期刊质量评价体系中的重要指标,直接反映着期刊影响力的高低。随着不断的应用,影响因子越来越受到重视,在科研相关行业发挥的导向作用不断增强,并且期刊影响因子的大小每年都会发生变化,因此提前了解相关学科SCI期刊影响因子的变化趋势,对相关科研工作者而言,可谓是一大帮助。灰色系统是指信息不完全的系统,它能够通过分析系统中现有的数据,包括数据中各种因素的关联性及其量的测度,然后运用灰数据映射方法处理随机量来挖掘规律,从而不断加深对系统的认识,最终挖掘出系统内在的变化规律。本文应用灰色系统理论中的GM(1,1)模型来分析SCI期刊13个学科大类与数学、肿瘤学小类专业影响因子的变化趋势,得出了SCI期刊13个大类学科与数学、肿瘤学小类专业影响因子变化的拟合模型,并根据拟合模型对未来几年13个大类学科与数学、肿瘤学小类专业影响因子的变化进行了预测。提前预测科技期刊的影响因子有诸多帮助:应用预测方法,计算出目标期刊新一年乃至以后的影响因子,有助于科技期刊编辑工作者正确把握科技期刊影响力的变化趋势;应用预测方法,提前预知期刊影响因子,有助于提高情报学研究工作者情报分析与预测的准确性,提升工作服务的时效性、针对性和有效性;应用预测方法,提前预知期刊影响因子,有助于提高科研评价研究人员进行科学科研绩效评价的合理性;应用预测方法,提前预知期刊影响因子,有助于提高科研人员投稿的针对性,有效性等。在影响因子应用越来越多的同时,也暴露出了很多应用方面的问题。例如,应用影响因子对期刊和论文质量进行评价时,在发表论文时间接近的情况下,运用影响因子对同一学科研究领域内的论文进行评价尚有其合理性,若时间前后差别过大则评价不合理。但对于不同学科领域,由于不同的学术环境、研究群体大小等多种影响因素各异,仅用单一的影响因子来作为跨学科研究领域论文学术影响力的评价标准,其合理性和科学性早已受到业内质疑。本文通过应用灰色系统GM(1,1)模型获得拟合数据,从数据均数可以初步看出13个大类之间以及数学小类与肿瘤学之间差异明显,通过对13个大类学科进行秩和检验,以及对数学小类与肿瘤学之间进行两独立样本T检验,差异都有统计学意义,更加说明不同学科影响因子大小存在差异,故仅用单一的影响因子来作为跨学科研究领域论文学术影响力的评价标准是不科学的。
[Abstract]:The influence factors play an important role in the citation analysis and are important indexes in the periodical quality evaluation system provided by JCR, which directly reflects the influence of journals. With the continuous application, more and more attention has been paid to the impact factors, and the guiding role in the scientific research related industries has been increasing, and the size of the journal impact factors will change every year. Therefore, to understand the trend of influencing factors of SCI journals in advance is a great help to relevant researchers. Grey system is a system with incomplete information. It can mine the rules by analyzing the existing data, including the correlation of various factors in the data and the measure of the quantity, and then using the grey data mapping method to deal with the random quantity. In order to deepen the understanding of the system, and finally excavate the inherent system changes. In this paper, the GM (1 / 1) model of grey system theory is used to analyze the change trend of influencing factors in 13 major disciplines and mathematics of SCI periodicals, and 13 major subjects and mathematics of SCI periodicals are obtained. According to the fitting model, the change of influence factors of 13 major subjects and mathematics and oncology in the next few years is predicted. It is helpful to predict the influence factors of sci-tech periodicals in advance: to calculate the influence factors of the target periodicals in the new year and beyond by using the prediction method, which is helpful for editors to grasp the changing trend of the influence of sci-tech periodicals; The application of prediction method to predict the influence factors of periodicals in advance is helpful to improve the accuracy of information analysis and prediction, to improve the timeliness, pertinence and effectiveness of work services, to apply the prediction method, to improve the accuracy of information analysis and prediction, to improve the effectiveness, pertinence and effectiveness of work services. Predicting the influence factors of journals in advance will help to improve the rationality of scientific research performance evaluation for researchers, and the application of prediction methods to predict the influence factors of periodicals in advance will help to improve the pertinence of contributions by researchers. Effectiveness, etc. At the same time, many problems in application are exposed as more and more factors are applied. For example, when evaluating the quality of journals and papers by using the influence factors, it is reasonable to use the influence factors to evaluate the papers in the same subject research field when the publication time is close. If the time before and after the difference is too large, the evaluation is unreasonable. However, for different disciplines, due to different academic environment, research group size and other factors, only a single impact factor is used as the evaluation criteria for the academic influence of interdisciplinary research field. Its rationality and scientific nature have long been questioned by the industry. In this paper, the fitting data are obtained by using the grey system GM (1K1) model. From the mean of the data, we can see that there are obvious differences among the 13 major classes and between the mathematical subclasses and the oncology, and the rank sum test is carried out on 13 major disciplines. And the T-test of two independent samples between mathematical subclasses and oncology shows that the difference is statistically significant, which further indicates that there are differences in the size of influence factors in different disciplines. Therefore, it is not scientific to use a single influence factor as the evaluation criterion of academic influence in interdisciplinary research field.
【学位授予单位】：重庆医科大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：G353.1;G237.5

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