Gender and the invisibility of care on Wikipedia

IF 6.5 1区社会学 Q1 SOCIAL SCIENCES, INTERDISCIPLINARY

Big Data & Society Pub Date : 2023-07-01 DOI:10.1177/20539517231210276

Heather Ford, Tamson Pietsch, Kelly Tall

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引用次数: 0

Abstract

Digital platforms produce bias and inequality that have a significant impact on peoples’ sense of self, agency and life chances. Wikipedia has largely evaded the criticism of other algorithmic systems like Google search and training databases like ImageNet, but Wikipedia is a critical source of representation in our current era – not only because it is one of the world's most popular websites, but because its data are being used as training data for the AI systems that are increasingly used for decision-making. We conducted an analysis of Wikipedia biographies in a national context, comparing the temporality and subjects of notability between English Wikipedia and the Australian Honours system in order to understand Wikipedia's unique role in the production of notability over the site's 20-year history. Framing Wikipedia as an active producer (rather than a reflection) of notability, we demonstrate that women are more likely to be awarded a Wikipedia page after the award announcements or not at all if their contribution is for labour relating to the caring professions than if their service is for sports, arts and films, politics or the judiciary. We argue that Wikipedia's inability to recognise gendered care work as noteworthy is mirrored in its own practices.

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性别和维基百科上的隐蔽性

数字平台产生了偏见和不平等，对人们的自我意识、能动性和生活机会产生了重大影响。维基百科在很大程度上避开了谷歌搜索等其他算法系统和ImageNet等训练数据库的批评，但维基百科是我们当前时代代表性的重要来源——不仅因为它是世界上最受欢迎的网站之一，还因为它的数据被用作人工智能系统的训练数据，而人工智能系统越来越多地用于决策。我们在国家背景下对维基百科的传记进行了分析，比较了英语维基百科和澳大利亚荣誉制度的知名度的时间和主题，以了解维基百科在网站20年历史中产生知名度的独特作用。我们将维基百科视为知名度的积极生产者(而不是反映)，我们证明，如果女性的贡献是与护理专业相关的劳动，而不是为体育、艺术和电影、政治或司法服务，那么她们更有可能在奖项宣布后获得维基百科页面，或者根本没有。我们认为，维基百科无法承认性别护理工作值得注意，这反映在它自己的实践中。

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

Big Data & Society SOCIAL SCIENCES, INTERDISCIPLINARY-

CiteScore

10.90

自引率

10.60%

发文量

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.