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Women on the move for science, technology, engineering and mathematics: Gender selectivity in higher education student migration.

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Title: Women on the move for science, technology, engineering and mathematics: Gender selectivity in higher education student migration.
Authors: Narh, Ebenezer D.1 enarh@uwo.ca, Buzzelli, Michael1
Source: Higher Education Quarterly. Jul2024, Vol. 78 Issue 3, p745-765. 21p.
Subject Terms: *STEM education, *Higher education, *School enrollment, *Scholarships
Geographic Terms: Canada
Abstract: Despite the gendered rebalancing of enrolments in higher education (HE) in the West, the underrepresentation of women in science, technology, engineering and mathematics (STEM) disciplines persists. Gendered selectivity of field of study influences higher education student migration (HESM) and in turn sheds light on HE participation. Framed by gender intersectionality theories both in HE studies and migration scholarship, this paper uses innovative data to analyse the intersectional effect of gender and field of study on HESM in Canada. Based on Statistics Canada's postsecondary student information system for the 2019/20 academic year, Canadian interregional flow matrixes structured by gender, field and level of study are constructed and analysed. The results show compelling evidence of the influence of gendered differences in HESM when intersected with field and level of study. Notably, women pursuing STEM studies migrate significantly more than any other grouping (i.e. gender, field and level of study groupings). The paper concludes with a discussion of policy implications for the influence of HESM on community demographic make‐up and local labour markets, as well as future research including the need to understand gendered dimensions of migration intentions and motivations. [ABSTRACT FROM AUTHOR]
Copyright of Higher Education Quarterly is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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  Data: Despite the gendered rebalancing of enrolments in higher education (HE) in the West, the underrepresentation of women in science, technology, engineering and mathematics (STEM) disciplines persists. Gendered selectivity of field of study influences higher education student migration (HESM) and in turn sheds light on HE participation. Framed by gender intersectionality theories both in HE studies and migration scholarship, this paper uses innovative data to analyse the intersectional effect of gender and field of study on HESM in Canada. Based on Statistics Canada's postsecondary student information system for the 2019/20 academic year, Canadian interregional flow matrixes structured by gender, field and level of study are constructed and analysed. The results show compelling evidence of the influence of gendered differences in HESM when intersected with field and level of study. Notably, women pursuing STEM studies migrate significantly more than any other grouping (i.e. gender, field and level of study groupings). The paper concludes with a discussion of policy implications for the influence of HESM on community demographic make‐up and local labour markets, as well as future research including the need to understand gendered dimensions of migration intentions and motivations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Higher Education Quarterly is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=178532202
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              Text: Jul2024
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