基于大数据的高校科研评价的价值意蕴与适用构想*

Study on the Value Essence and Applicable Concepts of Scientific Research Evaluation in Universities Based on Big Data

摘要:

正确评价科研成果的“科学价值、技术价值、经济价值、社会价值、文化价值”是一项极其重要的工作。面向以前瞻性基础研究为主和支撑人才培养、学科建设的高校科研，传统的同行评议法、文献计量法等高校科研评价方法存在许多局限，评价工作成为一大难题。文章基于大数据在高校科研评价中的应用，通过文献研究，在综述国内外相关研究的基础上，探讨大数据在高校科研评价中的价值和适用环节。研究表明，大数据在采集非结构化和半结构化数据、挖掘隐性和滞后性科学价值、提高处理速度和实现可视化表达、促进科研评价方法和方法论创新发展等方面具有强大的功能和应用价值。在实现高校科研功能多重性的数据提取、科研重心基础性的学术价值判断、科研成果价值非显性的数据挖掘和围绕破除“SCI至上”、聚焦“五大价值”的导向性的指标体系构建等环节功能突显，适用性强。文章强调，要防止沦为“数据万能”的陷阱，对运用大数据开展科研评价应加以调节和控制。

Abstract:

It is an extremely important task to correctly evaluate the "scientific value, technical value, economic value, social value and cultural value" of scientific research achievements. For the university scientific research which mainly focuses on forward-looking basic research and supports talent training and discipline construction, there are many limitations in the traditional peer review method, bibliometric method and other university scientific research evaluation methods, which make the evaluation work a major challenge. The paper discusses the functions and applicable links of big data in university research evaluation on the basis of the review of relevant domestic and international research. The study shows that the big data has outstanding functions and advantages in collecting unstructured and semi-structured data, mining hidden and lagging scientific values, processing speed and visual expression, and promoting the innovative development of research evaluation methods and methodologies. The functions of big data also play an important role in the key link of university scientific research evaluation, such as data collection for multiple functions of university scientific research, basic academic value judgment of scientific research focus, non-obvious data mining of scientific research achievement value, and data collection for eliminating the view that "SCI is the highest" and construction of an indicator system that focuses on the "five values"-oriented.The paper also emphasizes that it is significant to regulate and control the use of big data for scientific research evaluation to prevent the trap of "data is all-powerful" .