This presentation introduces community-centred approaches to curating knowledge about cultural artefacts outside and beyond the Global North's hegemony over knowledge ecologies and socio-technical infrastructures. I will outline the processes and conceptual framework that led us to contribute bibliographic metadata on all Arabic periodicals published until 1930 to Wikidata, the world's largest public and open knowledge graph. The data set originated with the scholarly crowd-sourcing project Jarāʾid. It comprises information on more than 3.000 periodicals, about 2.700 editors, and almost 350 holding institutions, gathering desperate information on what was published, when, where, and by whom, where to find it and if there are any known digital remediations. As a living union list of Arabic periodicals, the data set addresses the infrastructural weaknesses of library catalogues and discovery systems, as well as the epistemic violence of knowledge ecologies. These are exemplified by the absence of and lack of support for original scripts and languages in existing cataloguing data, interfaces, and search algorithms. Geo-fenced and and pay-walled platforms inaccessible to the societies of origin are another common feature. While the data set allows to answer questions such as "which periodicals could have reported on the series of urban food riots across the Eastern Mediterranean in summer 1910 and where would I find surviving copies?", formalising and executing such queries requires significant technical infrastructures and practical skills. We demonstrate how Wikidata addresses all these issues in a five-star, linked-open-data environment with full support for multilingual data and interfaces, robust user management and version control. While this already surpasses anything even a lavishly funded research project in the humanities could achieve, the active user base of human and robotic editors is arguably its greatest strength. Thanks to their independent and unconnected efforts, we have been able to significantly expand our shared knowledge and data set about the first Arabic mass medium.
Building similarity graph...
Analyzing shared references across papers
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Till Grallert (Wed,) studied this question.
synapsesocial.com/papers/6a250cd27def13d035e1d0e8 — DOI: https://doi.org/10.17613/6xekb-a4122
Till Grallert
Humboldt-Universität zu Berlin
Humboldt-Universität zu Berlin
National University of Formosa
Building similarity graph...
Analyzing shared references across papers
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