基于对《人民日报(2014-2018)》有关 "一带一路 "倡议文章的定量分析,探寻中国最高权力层的政治集团

Diana V. Filina
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引用次数: 0

摘要

本文主要探讨如何识别中国最高领导层中的政治团体,克服在尝试对政治人物进行分类时通常会出现的主要方法论困难。一种可能的方法是利用定量分析的方法,从中国主要官方大众媒体《人民日报》在其出版物和文章中提及政治人物的方式中找到规律,研究这些提及的分布情况。本研究选取了有关 "一带一路 "倡议的文章。从该主题出版物中提及人物的分布情况,我们可以区分出两类人。第一组以中国国家主席习近平为中心,第二组以中国国务院总理李克强为中心。本研究采用的媒体出版物信息分析方法的原则一般由 V.G. Gelbras 提出。将人物分为若干组的方法是以 D.V. Deopik 有关分类问题的著作为基础的。研究的结果是对政治人物进行了量化分组,并制定了一种方法,不仅可以确定中华人民共和国最高权力机构人物的政治派别,还可以确定那些处于较低层次的人物的政治派别,即那些在公开来源中信息很少的人物的政治派别。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
On Searching for Political Groups in the Highest Levels of Power in PRC Based on Quantitative Analysis of the Articles of Renmin Ribao (2014-2018) Devoted to the Belt and Road Initiative
The paper is focused on the problem of identifying political groups inside the top levels of the PRC leadership, overcoming the main methodological hardships that emerge usually when an attempt at a classification of political actors is made. One possible way to do it would be to use the methods of quantitative analysis to locate a pattern in how the chief Chinese official mass media Renmin Ribao mentions the political actors in their publications and articles, in studying the distribution of such mentions. The articles on the Belt and Road Initiative were selected for this research. The distribution of the mention of personalities in publications on this topic allows us to distinguish two groups. The first is formed around Chinese President Xi Jinping, the second around Li Keqiang, Premier of the State Council of the PRC. The principles of the approach to the analysis of information in media publications used in this study were generally formulated by V.G. Gelbras. The methods of distributing personalities into groups are based on the works of D.V. Deopik related to classification problems. The result of the study was a quantitatively substantiated grouping of political actors and formulation of a method to determine the political affiliation not only of figures from the highest echelons of power in the PRC, but also of those who are at lower levels, that is, those about whom there is little information in the open sources.
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