variabel terbaik yang berhubungan dengan emansipasi wanita p23

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variabel terbaik yang berhubungan dengan emansipasi wanita p23

yuliuseka

24-05-2024 11:02

variabel terbaik yang berhubungan dengan emansipasi wanita p23

Abstract
Enhancing Women's Participation in Global Value Chains through Machine Learning: A Framework for Economic Inclusion and Growth

The integration of women into global value chains (GVCs) is recognized as a pivotal strategy for fostering economic inclusion and sustainable growth. However, persistent challenges, such as limited access to education and training, gender-based discrimination, and data complexity, hinder efforts to realize the full potential of women's participation in GVCs. In this study, we propose a novel framework leveraging machine learning to address these challenges and enhance women's economic empowerment. Through a comprehensive literature review and theoretical analysis, we explore the multifaceted dynamics of women's participation in GVCs and the transformative potential of machine learning technology. Building upon established economic theories and methodologies, we present a methodological approach that integrates various machine learning algorithms, including linear regression, polynomial regression, support vector regression, random forest regression, and neural networks, to analyze GVC data and inform evidence-based policies. By harnessing the predictive power of machine learning, our framework aims to identify barriers to women's participation, forecast labor market trends, and design targeted interventions to promote gender equality and economic growth. This research contributes to the emerging field of gender-inclusive development and underscores the importance of leveraging technological innovations to advance women's economic empowerment in the context of GVCs.

Keywords: Women's participation, Global Value Chains (GVCs), Economic inclusion, Machine learning, Gender equality, Sustainable growth.

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