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基于“好干部标准”的干部考核评价:模型建构与指标体系 被引量:12

Assessment and Evaluation on the Basis of "Good Cadre Criterion" :Model Construction and Index System
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摘要 通过开放式问卷调查、深度访谈、专家论证等方法,收集并修订领导干部评价的内容条目,建构基于"好干部标准"的考核评价结构模型。研究结果表明,"好干部标准"反映了新的历史时期干部群众对"好干部"的内隐认知,是对优秀领导干部期待与诉求的"实然"和"必然"。好干部评价的结构模型是一个包含"信念坚定""为民服务""勤政务实""敢于担当"和"清正廉洁"的五维度模型。研究基于五维度结构模型构建了考核评价指标体系,五个一级指标的权重系数由大至小依次为"信念坚定""敢于担当""清正廉洁""为民服务""勤政务实",并围绕好干部评价模型的时代内涵、五个指标的验证式考察及综合指标体系的构建开展了讨论。 Through open-ended questionnaire, in-depth interview, expert review and other means, this study collects and revises cadres' evaluation contents, and constructs the evaluation model based on the criterion of good cadres. The result shows that the criteria of good cadres reflect the implicit cognition of the masses on good cadres in the new historical period, and represent the expectations and appeals for good cadres. The evaluation model of good cadres is a five-dimensional model that covers being firm in their ideals and convictions, serving the people, being diligent in work, being ready to take on responsibilities, as well as being honest and upright. In the evaluation index system of good cadres, the weight coefficients of the five dimensions ranged in a descending order as follows: being firm in their ideals and convictions, being ready to take on responsibilities, being honest and upright, serving the people, and being diligent in work. Furthermore,the time connotation of the evaluation model, the validation oriented evaluation of the five dimensions and the construction of comprehensive index system are discussed in the study.
作者 李明 郭庆松
出处 《中共中央党校学报》 CSSCI 北大核心 2018年第1期51-59,共9页 Journal of The Party School of The Central Committee of The C.T.C
基金 国家社会科学基金青年项目"领导干部考核评价机制研究"(14CDJ012)
关键词 好干部标准 考核评价 评价模型 指标体系 Good Cadre Criterion, Assessment and Evaluation, Evaluation Model, Evaluation Index System
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  • 1凌文辁,陈龙,王登.CPM领导行为评价量表的建构[J].心理学报,1987,19(2):199-207. 被引量:113
  • 2[18]Schapire R E,Singer Y.Improved boosting algorithms using confidence-rated predictions[J].Machine Learning,1999,37(3):297 -336.
  • 3[19]Fan W,Stolfo S J,Zhang J,et al.AdaCost:misclassification cost-sensitive boosting[C]//Bratko I,Dzeroski S.Proc of the 16th Intern Conf on Meachine Learning.Morgan Kanfmann,1999:97-105.
  • 4[20]Joshi M V,Kumar V,Agarwal R C.Evaluating boosting algorithms to classify rare classes:comparison and improvements[C]// Cercone N,Lin T Y,Wu X.Pro of the 2001 IEEE Intern Conf on Data Mining.Washington DC:IEEE Computer Society Press,2001:257 -264.
  • 5[21]Chawla N V,Japkowicz,Kolcz A.Editorial:special issue on learning from imbalaneed data sets[J].SIGKDD Explorations Special Issue on Learning from Imbalanced Datasets,2004,6(1):1 -6.
  • 6[22]Chawlal N V,Lazarevic A,Hall L O.SMOTEBoost:improving prediction of the minority class in boosting[C]// The 7th European Conf on Principles and Practice of Knowledge Discovery in Databases.Berlin:Springer,2003:107-119.
  • 7[23]He Guoxun,Han Hui,Wang Wenyuan.An over-sampling expert system for learning from imbalaneed data sets[J].Neural Networks and Brain,2005,1:537 -541.
  • 8[25]Tao Ban,Shigeo Abe.Implementing multi-class classifiers by one-class classification methods[C]// 2006 International Joint Conference on Neural Networks Sheraton Vancouver Wall Centre Hotel.Vancouver,BC:IEEE Press,2006:16 -21,327 -332.
  • 9[26]Sun Y.Cost-sensitive boosting for classification of imbalanced data[D].Canada:University of Waterloo,2007.
  • 10[27]Constantinopoulos C,Likas A.Semi-supervised and active learning with the probabilistic RBF classifier[J].Artificial Neural Networks,2008,71(13):2489-2498.

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