ABSTRACT
While the global development agenda has prioritized gender equality, many challenges remain, and the COVID-19 crisis has exacerbated inequalities. Gender transformative approaches to social change have the potential to address the underlying causes of inequality. This paper draws insights from studies funded by Canada's International Development Research Centre to understand how integrating gender transformative approaches to research can support social change. The findings suggest that gender transformative research is most successful in supporting change when it analyzes and addresses the multiple causes of inequality, takes an intersectional and structural approach, embeds the research in local contexts, and engages power holders and perpetrators of inequality.
ABSTRACT
The Coronavirus disease 2019 SARS-CoV-2 is a disease which causes fear to human lives that has taken thousands and hundreds of lives globally. The pandemic which has resulted in a global health emergency is currently a much sought-after research topic. The frequently mutating virus which has originated from Chiroptera and subsequently got transmitted to other mammals including humans. However, at the genomic level, it is yet to be unraveled what makes humans more prone to getting infected by the coronaviruses. Here, we have implemented a Machine Learning model known as K-means Clustering that uses the combination of different features to determine the risk of infection. In this research paper, the K-means clustering method is used since it is a good performer for Clustering analysis. The algorithm can group the sequences of the dataset into five clusters based on the Elbow plot and co-linearity of co-efficient. Using dimensional reduction technique PCA is used with a 3D visualization and a heat map to showcase the correlation efficiency between the mutated and original sequence considered. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Work from home (or remote working) has become the new normal ever since the pandemic hit the world. This new normal, which represents the unison of social and technical assemblage, has been used as a backdrop in the study to explore the nature of employee job behaviour and its impact on job satisfaction. The sample consisted of employees who are working remotely from their homes in the information technology sector. The Cronbach alpha of the questionnaire was found to be .862. The results indicate that dimensions of employee job behaviour like enhanced work association, need for interaction for information exchange, and increased work responsibility were found to be highly correlated in the backdrop of work from home. Further, employee job behaviour was found to be significantly impacting job satisfaction of employees in the backdrop of work from home. Copyright © 2022, IGI Global.