Opening Word document…

Flawed knowledge produces flawed results – without critically analyzing the epistemologies behind the 1AC women remain underrepresented.

Bartow 21 [(Ann Bartow, Cornell Graduate and Former Professor of Law at the University of New Hampshire School of Law) “Chapter 47 of ‘Handbook of Intellectual Property Research: Lenses, Methods, and Perspectives by Irene Calboli and Maria Lillà Montagnani’ Feminist Methodologies and Intellectual Property, Pages 775-776, Oxford Academic, May 20 2021” https://academic.oup.com/book/41122] hwkm

Viewing patents, copyrights, and trademarks through careful observation, the most important feminist methodological tool, presents clear data that women are underfunded and underrepresented in patents, copyrights, and trademarks, even as some of those trademarks are clearly gendered. Analyzing IP through the lens of feminist legal theory allows a clearer view of the problems of gender disparities within each of the IP fields.

If we fail to look critically at the patriarchal forces operating within our culture, they grow stronger in the shadows. When discreet groups are excluded from inventorship, authorship, or the marketing and branding channels that traffic in trademarks, everybody loses except for the inferior industry players who try to use IP law as a means to repress competition. We forfeit the missing inventions and creative works that people shut out from the necessary tools and resources cannot produce. And we lose the businesses they might have started, and the mentorships that could have followed their successes.

Women especially suffer when men are the default, and everything is planned and developed around them: Software developed by men may not anticipate hacking abuse from angry online trolls whom they, unlike most women, may not have experienced on the internet; innovative furniture that is built for men may be too high, large, or heavy for most women; medical studies that do not include women jeopardize our health; and artificial intelligence that uses biased data will bake discrimination into everything it interacts with. Identifying gender imbalances in IP contexts is only a beginning, but it is a necessary one.

Sign in to recut, update, or save this card.