Abstract
<title>Abstract</title> This study examines Pakistan’s marginalized local communities, such as the Hazara community, tribal women and the endangered Sindhi community suffering from floods. The study highlights how socio-political, economic and environmental factors exacerbate their exclusion. Through both qualitative and quantitative analysis of Pakistan’s social and living standards measurements (PSLM) data using machine learning techniques, findings reveal that the Hazara community faces religious violence and state neglect, tribal women face gender discrimination and Sindhi flood survivors endure resource crisis management from the government. The quantitative analysis uses 120 socioeconomic indicators from four provinces of Pakistan (Punjab, Sindh, Balochistan, and Khyber Pakhtunkhwa) along with Pakistan’s national aggregate and applies Principal Component Analysis (PCA), which reveals distinct variance patterns. Through clustering, this study identifies three key groups: infrastructure access, sanitation/governance and water access. The regression analysis suggests that governance reforms cannot resolve systemic discrimination. This paper offers an insight into a scalable methodology combining dimensionality reduction and unsupervised learning that can be useful to social policy researchers. It emphasises the need for a comprehensive approach to address socio-economic and governance challenges in marginalized communities.
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Publication Info
- Year
- 2025
- Type
- article
- Citations
- 0
- Access
- Closed
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- DOI
- 10.21203/rs.3.rs-8292426/v1