The Global STEM Workforce: Multilingualism as a Catalyst for Gender Inclusivity in Research and Innovation
Abstract
In the globalized landscape of Science, Technology, Engineering, and Mathematics (STEM), the need for diversity and inclusion has never been more critical. Despite strides toward gender equality, women remain underrepresented in STEM research and innovation. This article explores how multilingualism can act as a powerful catalyst for fostering gender inclusivity in STEM. By enhancing cross-cultural communication, breaking down language barriers, and creating new pathways for collaboration, multilingualism enables a more inclusive, dynamic, and diverse STEM workforce. Through case studies and actionable strategies, this article demonstrates how leveraging multilingualism can bridge the gender gap in STEM, empowering women to lead and innovate on the global stage.
References
1. Esfahani, M. N. Breaking Language Barriers: How Multilingualism Can Address Gender Disparities in US STEM Fields.
2. Nasr Esfahani, Mahshad. (2023). Breaking Language Barriers: How Multilingualism Can Address Gender Disparities in US STEM Fields. International Journal of All Research Education & Scientific Methods. 11. 2090-2100. 10.56025/IJARESM.2024.1108232090.
3. Mallinson, C., & Charity Hudley, A. H. (2014). Partnering through science: Developing linguistic insight to address educational inequality for culturally and linguistically diverse students in US STEM education. Language and Linguistics Compass, 8(1), 11-23.
4. Emerick, M. R. (2022). Supporting multilingual students from policy to practice: Systemic initiatives to create equitable career and technical education. International Multilingual Research Journal, 16(3), 209-216.
5. Curtis, J. H. (2021). Multilingualism and teacher education in the United States. Preparing teachers to work with multilingual learners, 191-215.
6. Madavarapu, Jhansi Bharathi, "Payroll Management System" (2014). All Capstone Projects. 82.
https://opus.govst.edu/capstones/82
7. Madavarapu, J. B. (2014). Payroll management system.
8. Charankar, N. (2022). FAULT TOLERANCE TECHNIQUES IN API AND MICRO SERVICES. International Journal on Recent and Innovation Trends in Computing and Communication, 10(12), 275-85.
9. Pandiya, D. K. (2022). Performance Analysis of Microservices Architecture in Cloud Environments. International Journal on Recent and Innovation Trends in Computing and Communication, 10(12), 264-74.
10. Thatoi, P. (2014). Characterization of generated voltage, current, power and power density from cow dung using double chamber microbial fuel cell (Doctoral dissertation).
11. Shaikh, J. M. (2004). Measuring and reporting of intellectual capital performance analysis. Journal of American Academy of Business, 4(1/2), 439-448.
12. Jaiswal, A., Singh, S., Wu, Y., Natarajan, P., & Natarajan, P. (2021, July). Keypoints-aware object detection. In NeurIPS 2020 Workshop on Pre-registration in Machine Learning (pp. 62-72). PMLR.
13. Nirmala, J. (2018). Empirical Testing of Target Adjustment Model of Capital structure: A Study on Indian Power Sector. International journal of management, technology and engineering, 10, 399.
14. Zanke, P., Raparthi, M., & Vyas, B. (2022). Predictive Modelling for Insurance Pricing: A Comparative Analysis of Machine Learning Approaches. Blockchain Technology and Distributed Systems, 2(2), 11-34.
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Copyright (c) 2023 Ahmed Mohandas, Mike Stephen
This work is licensed under a Creative Commons Attribution 4.0 International License.
