| 李光文 左 妍.RCC 视角下专家评审能力指标体系构建与实证研究[J].中国科技资源导刊,2025,(4):20~28 |
| RCC 视角下专家评审能力指标体系构建与实证研究 |
| Construction and Empirical Research on the Competence Indicator System for Experts from the RCC Perspective |
| 投稿时间：2025-06-26 |
| DOI： |
| 中文关键词: “负责任”评审;专家发现;专家评价;科技项目管理;逼近理想解排序法 |
| 英文关键词: RCC review, expert discovery, expert evaluation, technology project management, TOPSIS |
| 基金项目:天津市科技发展战略研究计划项目“天津市科技项目评审管理机制改革对策研究”(24ZLRKZL00170)。 |
| 作者 | 单位 | | 李光文 左 妍 | (天津市科学技术信息研究所，天津 300074) |
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| 摘要点击次数: 20 |
| 全文下载次数: 45 |
| 中文摘要: |
| 针对科研项目评审专家抽取和科技管理要求，提出一种多维数据驱动的“小同行”专家评价方法。首先，基于政策内容分析法和关键成功因素法，从科技计划项目评审政策文件中提取评审要求和关键要素，构建以学术水平、评审态度和评审能力为一级指标的多层次评价指标体系;然后，采用层次分析法确定指标权重，利用逼近理想值排序法计算评价得分，最终通过评价得分遴选出真懂此行此项、公正评审的领域专家;最后，以天津市75名专家的评审数据为例进行实验，发现这个方法能够较好地体现专家自身的科研业务素养。数据驱动的“负责任”评审专家指标体系有助于揭示专家的评审公正性、鉴别力等深层次隐性信息，为破“五唯”背景下高质量专家库建设和科研项目评审风险早期预警提供有益参考。 |
| 英文摘要: |
| A multidimensional data-driven “small peer” approach for expert evaluation is proposed to meet the requirements of expert selection and management for science and technology projects. Firstly, based on the policy content analysis method and the critical success factor method, the evaluation requirements and key elements are extracted from the policy documents, and a multi-level evaluation index system is constructed using academic level, evaluation attitude, and evaluation ability as the first level indicators. Then, the Analytic Hierarchy Process is used to determine the weights of index, and the ranking method that approximates the ideal value is used to calculate the evaluation score. Finally, based on the evaluation score, experts who truly understand project topic and fairly conduct evaluation are selected. With 75 evaluation records of Tianjin experts are used for verification, it is found that this method can effectively reflect the scientific literacy of experts. The data-driven “responsible” index system contributes to reveal impartiality, discernment, and other potential information of experts, and under the background of breaking “Five Unifications”, provides reference for construction of high-quality expert database and early warning of risks in scientific research project evaluation. |
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