王心怡,杨泽森

• 1:清华大学社会科学学院政治学系
• 2:暨南大学公共管理学院/应急管理学院

摘要(Abstract)：

地方主官是影响地方治理的关键行动者，然而关于其履职方式的基础性知识还有待推进。本文系统收集了2010—2019这10年间中部某省A市多位主政官员履职行为的党政大事记，使用自然语言处理技术和结构主题模型方法，识别出20种关键的地方主官履职行为。结合协变量建模分析，研究发现地方主官的履职行为受到宏观政治经济因素导致的趋势性、任期因素形成的周期性和个人特质带来的随机性这三种因素影响，但这种影响在不同类型的履职行为中存在显著差异。本文通过对极具史料价值的地方党政记录进行分析，揭示了地方主官履职行为的基本规律，为检验既有研究关于官员行为的理论猜想提供了新证据。同时，这一研究还将对地方主官的细颗粒度分析从注意力研究进一步拓展到行为研究，为理解官员行为与治理结果之间的关系链条作出贡献。

关键词(KeyWords)： 官员履职行为;政治精英;机器学习;地方主官

Abstract:

Keywords:

基金项目(Foundation): 国家社会科学基金重大项目“基于大数据的智能化社会治理监测、评估与应对策略研究”(项目批准号:18ZDA110)的资助

作者(Author): 王心怡,杨泽森

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• (1)A市所在省份共辖17个地级市,其中13个市的党史部门建立了官方网站。在这13个网站中,仅有A市长期整理、发布党政主官的行为大事记。在对全国其他地方的查找检索中也未能发现有地方发布类似内容。这体现了本文所应用的大事记数据的稀有性、珍贵性。 (2)数据源自世界银行:http://search.worldbank.org/data?qterm=time%20dealing&language=EN (3)为了更好地比较结构主题模型和生成式大模型在主题判别上的表现,参照TopicGPT框架(Pham et al.,2023),我们在投喂给GPT的提示词(prompt)中列举了前文人工识别出的主题名称,要求它根据“主题对文本最大概括、主题之间最大区分”的原则判断履职文本最可能的主题归属,并在必要时给出新的主题。 (4)图5的2020表示2020年的第一天,即此段曲线对应的分析文本是2019—2020年。为排除新冠疫情的扰动,本文所分析的大事记文本不包含2020年之后的。图9同。 (5)中国共产党新闻网.党内不容“码头文化”.详见http://mtw.so/62MGaa. (6)新华社.中共中央国务院关于打赢脱贫攻坚战三年行动的指导意见.详见https://www.gov.cn/gongbao/content/2018/content_5317095.htm. (7)学习时报.为什么说中国共产党是马克思主义使命型政党.详见https://baijiahao.baidu.com/s?id=1679050138376777019&wfr=spider&for=pc.