摘要
为破解高校实验室安全隐患“屡查屡犯”的难题,系统梳理了该现象的潜在安全风险、成因与治理痛点。研究发现,“屡查屡犯”现象易引发“破窗”效应,削弱安全管理效能,并叠加新旧隐患风险。究其原因:个体因素存在安全认知偏差、效率优先及责任缺失;组织架构层面则暴露出三方协同短板。为此,构建集隐患排查数据智能分析-“隐患分类与责任认定-预警惩处与整改”于一体的治理系统。实践表明,该系统能准确定位“屡犯”隐患特征、明确隐患类别与责任主体,推动多方联合整改与治理,显著降低“屡犯”隐患存量。研究结果可为高校实验室安全管理提供可借鉴的范式。
To enhance the governance capacity for the recurring safety noncompliance phenomenon in university laboratories,an in-depth analysis was conducted on the potential safety risks,root causes,and governance challenges associated with this phenomenon.The research reveals that the recurring safety noncompliance phenomenon can easily trigger the“broken windows”effect,reduce the effectiveness of safety management,and lead to the compounding of old and new hazard risks.The primary causes of this issue include individual factors such as cognitive biases in safety awareness,prioritization of efficiency,and lack of accountability,as well as organizational shortcomings at the structural level.Based on this analysis,a new governance system was developed,consisting of intelligent analysis of hazard investigation data,hazard classification and responsibility determination,as well as early warning,punishment,and rectification mechanisms.Practical applications demonstrate that this system can accurately identify the characteristics of recurring hazards,clarify hazard categories and responsible parties,promote collaborative governance among stakeholders,and effectively reduce the number of repeated hazards.The research results can provide reference for the safety management of university laboratories.
作者
江凯禧
林兆花
高丽娟
肖军
张润发
JIANG Kaixi;LIN Zhaohua;GAO Lijuan;XIAO Jun;ZHANG Runfa(Office of Laboratory Safety and Equipment Management,Zhuhai Campus of Beijing Normal University,Zhuhai 519087,Guangdong,China)
出处
《实验室研究与探索》
北大核心
2025年第8期249-254,共6页
Research and Exploration In Laboratory
基金
中国高等教育科学研究规划课题重点项目(24SY0211)
广东省高等教育学会实验室管理专业委员会2024年基金项目(GDJ20240001)
北京师范大学2024年度研究生综合素养课程教改项目(110115113)。
关键词
安全隐患
屡查屡犯事件
治理体系
人工智能
safety hazards
recurrent safety noncompliance accidents
governance system
artificial intelligence
