Abstract
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.
Highlights
Have we embraced complacency and become too comfortable with the internet’s knowledge production capabilities? If so, by choosing to rest on our laurels and exploit this affordance, what happens to epistemic curiosity? (D’Arnault, 2019)For all their rhetorical flair, these questions raised by Digital Culturalist blogger Clayton D’Arnault force us to face an inconvenient reality
The critical examination of representations of politicians on these platforms is an active area of research. Recent scholarship in this domain has for instance uncovered that search results for politicians in Google and Wikipedia can be biased for gender and party identity (Pradel, 2020), and that editors of politicians’ pages tend to focus on particular parties and choose references from specific news outlets (Agarwal, Redi, Sastry, Wood, & Blick, 2020)
One might for instance choose to analyse the technological particularities of MediaWiki implementations such as Wikipedia or Wikidata as software platforms (MediaWiki, 2020a,b), investigate systemic bias (Martin, 2018; Oeberst, von der Beck, Cress, & Nestler, 2019), discuss the philosophical, sociological or economic foundations and impact of a free, open software movement (Tkacz, 2015), or explore the whole of Wikipedia or Wikidata content supported by big data approaches (Farda-Sarbas & Müller-Birn, 2019; Schroeder & Taylor, 2015)
Summary
Have we embraced complacency and become too comfortable with the internet’s knowledge production capabilities? If so, by choosing to rest on our laurels and exploit this affordance, what happens to epistemic curiosity? (D’Arnault, 2019). Criticisms have for instance been levelled at Google’s opaque ranking and rating algorithms (Wakabayashi, 2017), and an overreliance on the use of Google-like search engines fosters what Lynch (2016) describes as ‘Google knowing’, a form of knowledge-seeking that precludes critical comparisons between sources, and which boils down to following the opinion of the majority. Along those lines, Wikipedia has been shown to be a battleground for. Recent scholarship in this domain has for instance uncovered that search results for politicians in Google and Wikipedia can be biased for gender and party identity (Pradel, 2020), and that editors of politicians’ pages tend to focus on particular parties and choose references from specific news outlets (Agarwal, Redi, Sastry, Wood, & Blick, 2020)
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