Policymakers and scholars are increasingly concerned with how nanotechnology can reduce inequalities and provide benefits for disadvantaged and poor communities. This paper simultaneously addresses two concerns related to nanotechnology and equity: the lack of research and development focused on nanotechnology applications that benefit developing nations (pro-poor R&D) and the lack of women in nanotechnology fields. The paper focuses on Canada, an affluent country committed to both pro-poor and gender responsive policies. Social network analysis is used to examine the relationship between gender and collaboration patterns of authors and inventors whose work is related to pro-poor applications of nanotechnology. Findings reveal that female first-authored papers have a lower citation rate and are published in higher ranked journals compared to those papers first-authored by men. Nevertheless, when women are last or corresponding authors, their papers receive equal or higher citation rates and are published in lower or similar ranked journals. Women are as, or more, collaborative as their male peers in their co-authorship and co-inventorship networks. While the majority of male authors and male inventors collaborate exclusively with men, those involved in a mixed-gender team outperform male-only teams. Women, as both authors and inventors, are involved in more gender-balanced collaboration teams. The study calls for development and implementation of gender-related policies in Canada to increase the prevalence of female scientists in collaboration networks, and to support the participation of women in pro-poor areas.
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For instance, a policy that focuses on economic inequality and increasing investments in pro-poor areas, alone, might exacerbate the underrepresentation of women and gender inequalities in STEM fields as an unintended consequence.
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Ghiasi, G., Harsh, M. & Schiffauerova, A. Inequality and collaboration patterns in Canadian nanotechnology: implications for pro-poor and gender-inclusive policy. Scientometrics 115, 785–815 (2018). https://doi.org/10.1007/s11192-018-2701-2
- Social network analysis