Questions, Strategies and Index Systems on Evaluation of Academic Output for Philosophy and Social Sciences in the Big Data Era postprint

Abstract: [Purpose/significance]To solve some defects of peer review and scientometrics methods in the evaluation of philosophy and social sciences academic outputs, the reform and realization of the evaluation of the academic achievements are explored, especially the evaluation index system of the academic achievements of philosophy and social sciences based on the big data thinking are designed.[Method/process]Based on the comparative analysis and comprehensive analysis, this paper analyzes the disadvantages of the traditional philosophy and social sciences evaluation methods, and then analyzes the changes brought by the big data to the philosophy and social sciences evaluation, finally, it puts forward the philosophy and social sciences evaluation strategies and the index systems based on the big data environment.[Result/conclusion]In big data era, it is possible to analyze the semantics and its relevance based on all-round academic contents and activity data. By using the citation content and behavior evaluation, the academic activities-centered whole process dynamic evaluation, academic value and social function evaluation, value of academic achievements of philosophy and social sciences can be truly, comprehensively and objectively reflected. Based on above research, an index system for evaluation of academic achievements of philosophy and social sciences is constructed, which is composed of two first-level indicators, five two-level indicators and 34 three-level indicators.