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Data Feminism in DH: Hackathon und Netzwerktreffen

Authors: Lang, Sarah; Borek, Luise; Probst, Nora;

Data Feminism in DH: Hackathon und Netzwerktreffen

Abstract

"Das Ziel von Data Feminism ist es, ausgehend vom intersektionalen Feminismus vorwiegend männlich dominierte Narrative in den Digital Humanities kritisch zu reflektieren. Häufig ist zu beobachten, dass sich der weiß, cis-männlich und hegemonial dominierte Bias aus Archiven und Quellen in daraus erhobenene Daten und digitale Technologien überträgt. Das 2020 von Catherine D'Ignazio und Lauren Klein veröffentlichte Buch Data Feminism (MIT Press) hat für die Digital Humanities hohe Relevanz, wendet sich aber an ein Data-Science-Publikum und bietet kaum unmittelbar anwendbare Strategien für die DH, was eine Einstiegshürde darstellt. Ziel dieses Workshops ist es einerseits, im Sinne eines Hackathons Ansätze des Data Feminism an eigenen Daten zu diskutieren und erproben, andererseits die Erarbeitung eines "How to Data Feminism in DH"-Manifests, in dem Einstiegsliteratur zitiert ist und die grundlegenden Positionen vermittelt werden. Veröffentlicht wird das Ergebnis der Arbeit auf dem DHd-Blog sowie als Working Paper der AG Empowerment, die diesen Workshop unterstützt." Ein Beitrag zur 9. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum" - DHd 2023 Open Humanities Open Culture.

Keywords

Strukturanalyse, Methoden, Einführung, Metadaten, Gender Data Gap, Modellierung, DHd2023, Datenfeminismus, Intersektionalität, Data Feminism, Korpuskritik, Daten

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This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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