<div class="csl-bib-body">
<div class="csl-entry">Damian, C., & Vana Gür, L. (2022, December 19). <i>Detecting Gender Bias in Children’s Textual Literature</i> [Conference Presentation]. 16th International Conference on Computational and Financial Econometrics (CFE 2022), London, United Kingdom of Great Britain and Northern Ireland (the).</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/153866
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dc.description.abstract
Gender stereotypes form early in the child’s development and are carried over throughout adolescence into adulthood, leaving long-lasting effects which may impact activity and career choices, as well as academic performance. Books, in particular, can have considerable influence, as their characters serve to shape role models of femininity and masculinity for young children. Thus, gender under- and misrepresentation in children’s
textual literature can contribute to the internalization and reinforcement of negative stereotypes. To address this issue, we aim to identify and measure relevant dimensions of gender bias in children’s books with the aid of both qualitative and quantitative techniques: systematic literature review across disciplines, synthesis and (expert) validation on the one hand and state-of-the-art NLP methods on the other. By exploiting such an integrated research framework, we believe that we can automate the detection of potentially biased text while enhancing the interpretability and transparency of the results.
en
dc.description.sponsorship
Fonds zur Förderung der wissenschaftlichen Forschung (FWF)
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dc.language.iso
en
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dc.subject
Gender bias
en
dc.subject
NLP
en
dc.title
Detecting Gender Bias in Children's Textual Literature
