Ethnography beyond thick data

IF 0.7 Q3 ANTHROPOLOGY
Ajda Pretnar Žagar, Dan Podjed
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引用次数: 0

Abstract

This article presents opportunities for enriching anthropological knowledge and methods with machine learning and data analysis. Different examples show how quantitative methods empower anthropologists and how computational methods supplement ethnography, from sensor data and interview transcripts to designing technology solutions and automatically labeling cultural heritage. Conversely, the authors discuss the benefits of qualitative approaches in contemporary anthropological research and show how to transition from data analysis to ethnography and vice versa. Finally, the article pinpoints aspects in which each method can fail individually. It discusses why a combination of the two approaches, called circular mixed methods, minimizes the chance of failure and maximizes insights from the data.

超越厚重数据的人种学
本文介绍了利用机器学习和数据分析丰富人类学知识和方法的机会。从传感器数据和访谈记录到设计技术解决方案和自动标注文化遗产,不同的例子展示了定量方法如何增强人类学家的能力,以及计算方法如何补充人种学。相反,作者讨论了定性方法在当代人类学研究中的优势,并展示了如何从数据分析过渡到人种学研究,反之亦然。最后,文章指出了每种方法各自可能失败的方面。文章讨论了为什么将这两种方法结合起来(称为循环混合方法)可以将失败的几率降到最低,并从数据中获得最大的启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
1.90
自引率
14.30%
发文量
21
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