对政府领导的在线知识表示进行挑剔。维基百科和维基数据中的比利时首相案例。

Q2 Social Sciences
LIBER Quarterly Pub Date : 2020-12-26 DOI:10.18352/lq.10362
Tom Willaert, G. Roumans
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引用次数: 0

摘要

求知者的一个关键陷阱,尤其是在政治领域,是见多识众的自满,或者以牺牲求知欲为代价过度依赖搜索引擎。最近的学术研究记录了公众最依赖的知识来源存在的重大问题,包括维基百科和谷歌存在意识形态和算法偏见的例子。这样的观察提出了一个问题,即在遇到类似的认识论问题之前,人们实际上需要深入挖掘这些平台对事实(历史和传记)知识的表示。本文通过在维基百科和维基数据中“挑剔”政府和政府领导的知识表示来解决这个问题。在新兴的“数据研究”框架中,我们对比利时首相及其政府的代表进行了微观层面的分析,从而揭示了分类、命名和联系传记项目的问题,这些问题远远超出了所讨论平台的能力范围。因此,本文对研究标志着人文学科知识形式化的基本挑战做出了基于证据的贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nitpicking online knowledge representations of governmental leadership. The case of Belgian prime ministers in Wikipedia and Wikidata.
A key pitfall for knowledge-seekers, particularly in the political arena, is informed complacency, or an over-reliance on search engines at the cost of epistemic curiosity. Recent scholarship has documented significant problems with those sources of knowledge that the public relies on the most, including instances of ideological and algorithmic bias in Wikipedia and Google. Such observations raise the question of how deep one would actually need to dig into these platforms’ representations of factual (historical and biographical) knowledge before encountering similar epistemological issues. The present article addresses this question by ‘nitpicking’ knowledge representations of governments and governmental leadership in Wikipedia and Wikidata. Situated within the emerging framework of ‘data studies’, our micro-level analysis of the representations of Belgian prime ministers and their governments thereby reveals problems of classification, naming and linking of biographical items that go well beyond the affordances of the platforms under discussion. This article thus makes an evidence-based contribution to the study of the fundamental challenges that mark the formalisation of knowledge in the humanities.
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来源期刊
LIBER Quarterly
LIBER Quarterly Social Sciences-Library and Information Sciences
CiteScore
2.20
自引率
0.00%
发文量
0
审稿时长
6 weeks
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