公共管理研究中的机器学习及其在组织声誉中的应用
Machine Learning for Public Administration Research, with Application to Organizational Reputation
摘要：
近年来，机器学习方法在公共行政学者和实践者中非常流行。这些技术为分析文本、图像和其他类型的数据打开了大门，使我们能够测试公共行政的基础理论并发展新的理论。尽管机器学习方法令人兴奋，但缺乏关于其正确使用和潜在陷阱的清晰度。本文试图通过为公共行政学者和实践者提供一个机器学习“实践指南”来填补文献中的这一空白。在这里，我们采取了机器学习的基本观点，并描述了这些方法如何能够丰富公共行政的研究和实践，通过他们的能力开发新的措施，挖掘新的数据来源，并进行统计推断和因果推理的原则性方式。然后，我们将注意力转向使用这些方法的陷阱，如未经验证的措施和缺乏可解释性。最后，我们展示了如何机器学习技术可以帮助我们了解组织声誉的联邦机构，通过一个说明性的例子，使用来自13个执行联邦机构的推文。
英文摘要：
Machine learning methods have gained a great deal of popularity in recent years among public administration scholars and practitioners. These techniques open the door to the analysis of text, image and other types of data that allow us to test foundational theories of public administration and to develop new theories. Despite the excitement surrounding machine learning methods, clarity regarding their proper use and potential pitfalls is lacking. This paper attempts to fill this gap in the literature through providing a machine learning "guide to practice" for public administration scholars and practitioners. Here, we take a foundational view of machine learning and describe how these methods can enrich public administration research and practice through their ability develop new measures, tap into new sources of data and conduct statistical inference and causal inference in a principled manner. We then turn our attention to the pitfalls of using these methods such as unvalidated measures and lack of interpretability. Finally, we demonstrate how machine learning techniques can help us learn about organizational reputation in federal agencies through an illustrated example using tweets from 13 executive federal agencies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
