Editorial introduction: Towards a machinic anthropology

IF 6.5 1区 社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY
M. Pedersen
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引用次数: 0

Abstract

Bringing together a motley crew of social scientists and data scientists, the aim of this special theme issue is to explore what an integration or even fusion between anthropology and data science might look like. Going beyond existing work on the complementarity between ‘thick’ qualitative and ‘big’ quantitative data, the ambition is to unsettle and push established disciplinary, methodological and epistemological boundaries by creatively and critically probing various computational methods for augmenting and automatizing the collection, processing and analysis of ethnographic data, and vice versa. Can ethnographic and other qualitative data and methods be integrated with natural language processing tools and other machine-learning techniques, and if so, to what effect? Does the rise of data science allow for the realization of Levi-Strauss’ old dream of a computational structuralism, and even if so, should it? Might one even go as far as saying that computers are now becoming agents of social scientific analysis or even thinking: are we about to witness the birth of distinctly anthropological forms of artificial intelligence? By exploring these questions, the hope is not only to introduce scholars and students to computational anthropological methods, but also to disrupt predominant norms and assumptions among computational social scientists and data science writ large.
编辑简介:走向机器人类学
本期专题将汇集社会科学家和数据科学家,旨在探讨人类学和数据科学之间的整合甚至融合可能是什么样子。超越现有的关于“厚”定性和“大”定量数据之间互补性的工作,我们的目标是通过创造性和批判性地探索各种计算方法来增加和自动化收集、处理和分析民族志数据,从而动摇和推动既定的学科、方法和认识论边界,反之亦然。民族志和其他定性数据和方法是否可以与自然语言处理工具和其他机器学习技术相结合,如果可以,会产生什么效果?数据科学的兴起是否允许实现列维-施特劳斯的计算结构主义的古老梦想,即使是这样,它应该吗?甚至有人可能会说,计算机现在正在成为社会科学分析甚至思考的代理人:我们是否即将见证独特的人类学形式的人工智能的诞生?通过探索这些问题,我们不仅希望向学者和学生介绍计算人类学方法,而且希望打破计算社会科学家和数据科学中的主流规范和假设。
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来源期刊
Big Data & Society
Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
10.90
自引率
10.60%
发文量
59
审稿时长
11 weeks
期刊介绍: Big Data & Society (BD&S) is an open access, peer-reviewed scholarly journal that publishes interdisciplinary work principally in the social sciences, humanities, and computing and their intersections with the arts and natural sciences. The journal focuses on the implications of Big Data for societies and aims to connect debates about Big Data practices and their effects on various sectors such as academia, social life, industry, business, and government. BD&S considers Big Data as an emerging field of practices, not solely defined by but generative of unique data qualities such as high volume, granularity, data linking, and mining. The journal pays attention to digital content generated both online and offline, encompassing social media, search engines, closed networks (e.g., commercial or government transactions), and open networks like digital archives, open government, and crowdsourced data. Rather than providing a fixed definition of Big Data, BD&S encourages interdisciplinary inquiries, debates, and studies on various topics and themes related to Big Data practices. BD&S seeks contributions that analyze Big Data practices, involve empirical engagements and experiments with innovative methods, and reflect on the consequences of these practices for the representation, realization, and governance of societies. As a digital-only journal, BD&S's platform can accommodate multimedia formats such as complex images, dynamic visualizations, videos, and audio content. The contents of the journal encompass peer-reviewed research articles, colloquia, bookcasts, think pieces, state-of-the-art methods, and work by early career researchers.
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