将民族志与机器学习结合，提升社会科学研究的深度与广度。
Ethnography and Machine Learning: Synergies and New Directions
- 融合质性田野与机器学习，拓展研究视野。
- 通过多项目实践验证方法可行性。
- 适合从事跨文化比较研究的学者参考。
民族志（揭示人们如何理解、应对并塑造其生活真实情境的社会科学方法）与机器学习（利用大数据和统计学习模型完成可量化任务的计算技术）各自是当代社会科学的核心工具。然而在实践中，两者长期分离。本文基于日益增长的学术成果，主张民族志与机器学习可有效结合，尤其适用于大规模比较研究。具体而言，本文（a）阐释了将机器学习与质性实地研究结合的价值与挑战，（b）探讨相关方法论趋势，（c）提供多个大型项目的实际工作流程案例，（d）最后提出推动田野方法与机器学习协同演进的路线图。
原文摘要 · Abstract (English)
Ethnography (social scientific methods that illuminate how people understand, navigate and shape the real world contexts in which they live their lives) and machine learning (computational techniques that use big data and statistical learning models to perform quantifiable tasks) are each core to contemporary social science. Yet these tools have remained largely separate in practice. This chapter draws on a growing body of scholarship that argues that ethnography and machine learning can be usefully combined, particularly for large comparative studies. Specifically, this paper (a) explains the value (and challenges) of using machine learning alongside qualitative field research for certain types of projects, (b) discusses recent methodological trends to this effect, (c) provides examples that illustrate workflow drawn from several large projects, and (d) concludes with a roadmap for enabling productive coevolution of field methods and machine learning.
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