Abstract:

[Purposes] This paper aims to accurately identify the data risks associated with the application of big data technology in academic journal publishing activities to provide a reference for implementing effective risk prevention strategies for academic journals. [Methods] The specific data risks faced by academic journals were identified by defining data risks, identifying the types of data affected and the relevant parties involved, and considering various stages of the publishing process. At the same time, according to the technical and legal factors contributing to data risks, the root causes of data risks were deeply analyzed. [Findings] Based on the identification of data risks and the in-depth analysis from technical and legal perspectives, it is concluded that academic journals need to prevent data risks from the subjective level, technical level, and legal level. [Conclusions] Academic journals should improve data identification and sensitivity, and adhere to data security awareness and confidentiality obligations at the subjective level; adopt appropriate data storage and processing solutions, and implement data classification and management at the technical level; improve data service contract terms, and avoid data ownership and copyright disputes at the legal level.

Key words: Big data, Academic journal publishing, Data risk, Technical prevention, Legal prevention

摘要：

【目的】 准确识别学术期刊出版活动应用大数据技术的数据风险,为学术期刊采取有效风险防范策略提供参考。【方法】 通过界定数据风险的定义、受影响的数据类型以及牵涉的相关主体,并结合各个出版环节,识别学术期刊面临的具体数据风险。同时根据数据风险的技术诱因与法律诱因,深入分析数据风险根源。【结果】 基于数据风险识别和技术与法律视角的根源分析,得出学术期刊需要分别从主体层面、技术层面和法律层面防范数据风险。【结论】 学术期刊应当在主体层面提升数据辨识度与敏感性、贯彻数据安全意识与保密义务,在技术层面匹配数据存储与处理方案、执行数据分级分类管理,在法律层面完善数据服务合同条款、避免数据权属与版权争端。

关键词: 大数据, 学术期刊出版, 数据风险, 技术防范, 法律防范