Utilizing Classification as a Tool to Address Gender Equality Issues in Science, Technology, and Innovation Policy - Caitlin Meyer; Du Baogui (SAGE Open)
Historically, the formulation of science, technology, and innovation policy (STIP) has exhibited a predominantly male perspective, neglecting the critical examination and measurement of gender bias and inequitable educational practices. These two factors significantly contribute to the underrepresentation of women and gender minorities in STIP frameworks. This pervasive global issue potentially hinders innovation within crucial sectors, particularly in addressing pressing challenges such as the coronavirus disease pandemic of 2019 (COVID-19) and climate change. To address this concern comprehensively, a simplified classification framework is proposed to assess gender bias and inequitable education, which consistently emerge as key factors across nations, leading to the lack of consideration of women and gender minorities in STIP. Despite the inherent complexities of the social sciences, leveraging these two well-established variables as foundational starting points can foster an iterative process tailored to each country’s context. Such a system has the potential to ameliorate the existing disparity, while concurrently striving to achieve gender parity within the STI landscape.