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Title: Gender Based Violence and Its Impact on the Society

Abstract: Gender-based violence (GBV) is a pervasive global issue that affects individuals across all demographics, but disproportionately impacts women and girls. This abstract examines the multifaceted nature of GBV, encompassing various forms such as physical, sexual, psychological, and economic violence. Using a socio-ecological framework, it explores the complex interplay of individual, relational, community, and societal factors that contribute to the perpetuation of GBV. Furthermore, the abstract delves into the profound consequences of GBV on individuals and society at large. Beyond the immediate physical and psychological trauma experienced by survivors, GBV perpetuates cycles of poverty, hinders economic development, and undermines social cohesion. It exacerbates existing inequalities and impedes progress towards gender equality and women's empowerment. Drawing on empirical evidence and theoretical frameworks, this paper underscores the urgent need for comprehensive, multi-sectoral approaches to address GBV effectively. Such approaches should encompass prevention, intervention, and response strategies that engage diverse stakeholders, including governments, civil society organizations, communities, and individuals. Moreover, efforts to combat GBV must be underpinned by a commitment to challenging harmful gender norms, promoting human rights, and fostering gender-equitable societies. By shedding light on the pervasive nature and far-reaching impacts of GBV, this abstract seeks to inform policy, advocacy, and programming efforts aimed at eradicating this grave violation of human rights and fostering a more just and equitable society for all.

By Faraha, Nazia Ansari
In Volume: 14,Issue: 1
Title: Social Intelligence and Burnout among Post Graduate Students

Abstract: This study was aimed to identify the effect of social intelligence on the academic burnout among college students who were studying in different faculties (Science, Commerce, Arts). The data was collected from different colleges situated in Meerut city. A total 300 students studying different colleges under CCS University were participated. Burnout was measured by Copenhagen(2012)’s Burnout Scale while Social Intelligence by Chadda and Ganeshan (2009). Multiple Regression was used to find our predictors for the burnout among college students. Regression Analysis revealed that social intelligence was emerged as important predictor of burnout. Further T test also revealed significance difference between groups. It was found that female students were having more burnout problems as compare to male participants. Students’ social intelligence is a Type a perceiving ability to understand social cues and effectively navigate social situations. It is ability to cope with burnout or stressors and maintain balance between academic and personal life. In this paper the present study has social applied application Academic Achievement, Mental health and general wellbeing can all be affected by social intelligence. Thus the present study is to examine low social intelligence affects college’s student abilities to handle their burnout problems.

By Pratiksha Rani, Manju Khokhar
In Volume: 14,Issue: 1
Title: Electric Mobility Integration in Indian Urban Planning: Challenges, Opportunities, and Policy Implications

Abstract: India’s urban transport system is facing unprecedented challenges due to rapid population growth, vehicular congestion, and escalating pollution levels. Against this backdrop, the transition toward electric mobility (e-mobility) offers a promising pathway for sustainable urban development. This study investigates the extent to which electric mobility is being integrated into urban planning in Indian cities. It explores critical challenges such as inadequate charging infrastructure, limited policy coordination, and citizen hesitancy. Drawing upon both primary data collected through surveys in five urban centres—and secondary sources from government and institutional reports, the research applies statistical methods, including factor analysis and regression modelling, to examine the drivers of electric vehicle (EV) adoption. The findings reveal that infrastructure readiness and public policy awareness are strong predictors of urban EV acceptance. The study concludes by offering practical policy recommendations, such as zoning reforms and enhanced fiscal incentives, aimed at creating EV-supportive urban environments aligned with national climate goals.

By Shantam Babbar, Rajesh Kumar Raju, Monika Kumari
In Volume: 14,Issue: 1
Title: Social Intelligence and Burnout among Post Graduate Students

Abstract: This study was aimed to identify the effect of social intelligence on the academic burnout among college students who were studying in different faculties (Science, Commerce, Arts). The data was collected from different colleges situated in Meerut city. A total 300 students studying different colleges under CCS University were participated. Burnout was measured by Copenhagen(2012)’s Burnout Scale while Social Intelligence by Chadda and Ganeshan (2009). Multiple Regression was used to find our predictors for the burnout among college students. Regression Analysis revealed that social intelligence was emerged as important predictor of burnout. Further T test also revealed significance difference between groups. It was found that female students were having more burnout problems as compare to male participants. Students’ social intelligence is a Type a perceiving ability to understand social cues and effectively navigate social situations. It is ability to cope with burnout or stressors and maintain balance between academic and personal life. In this paper the present study has social applied application Academic Achievement, Mental health and general wellbeing can all be affected by social intelligence. Thus the present study is to examine low social intelligence affects college’s student abilities to handle their burnout problems.

By Pratiksha Rani, Manju Khokhar
In Volume: 14,Issue: 1
Title: Ethical AI Integration and the Future of Employee Rights at Work

Abstract: Artificial Intelligence (AI) has become increasingly central to both economic progress and modern business practices. While much public discussion has centered on the societal and ethical dimensions of AI—particularly in relation to data privacy and human rights—there has been comparatively less attention on how AI is transforming traditional workplace dynamics, especially in the area of occupational health and safety. Although concerns about human rights and gig economy conditions are well-documented, the potential implications of AI for day-to-day worker safety remain underexplored. This paper seeks to fill that gap by introducing a conceptual framework for an AI Work Health and Safety (WHS) Scorecard. This tool is designed to help identify and manage workplace risks linked to AI deployment. Drawing from a qualitative, practice-oriented research project involving organizations actively implementing AI, the study outlines a set of health and safety risks derived from aligning Australia’s AI Ethics Principles and Principles of Good Work Design with the AI Canvas—a tool traditionally used to evaluate AI’s commercial value. The study’s key innovation lies in a newly developed matrix that maps known and anticipated WHS and ethical risks across each stage of AI adoption, offering a structured approach to evaluating AI’s workplace impact.

By Kanika Maheshwari
In Volume: 14,Issue: 1
Title: Electric Mobility Integration in Indian Urban Planning: Challenges, Opportunities, and Policy Implications

Abstract: India’s urban transport system is facing unprecedented challenges due to rapid population growth, vehicular congestion, and escalating pollution levels. Against this backdrop, the transition toward electric mobility (e-mobility) offers a promising pathway for sustainable urban development. This study investigates the extent to which electric mobility is being integrated into urban planning in Indian cities. It explores critical challenges such as inadequate charging infrastructure, limited policy coordination, and citizen hesitancy. Drawing upon both primary data collected through surveys in five urban centres—and secondary sources from government and institutional reports, the research applies statistical methods, including factor analysis and regression modelling, to examine the drivers of electric vehicle (EV) adoption. The findings reveal that infrastructure readiness and public policy awareness are strong predictors of urban EV acceptance. The study concludes by offering practical policy recommendations, such as zoning reforms and enhanced fiscal incentives, aimed at creating EV-supportive urban environments aligned with national climate goals.

By Shantam Babbar, Rajesh Kumar Raju, Monika Kumari
In Volume: 14,Issue: 1