In the realm of Diversity, Equity, and Inclusion (DEI), LinkingGlass proves to be an invaluable tool for dissecting data. Our focus in this instance centers on the Givitas tool, where individuals actively seek and provide assistance, fulfilling requests from their peers. LinkingGlass enables us to gain immediate insights into the dynamics of help-sharing within this network.
A crucial aspect we delve into is the identification of patterns in the interactions: Are males predominantly helping other males? Do females tend to assist fellow females? Or is the network characterized by a balanced exchange between both genders? Furthermore, we scrutinize the timing of these interactions – do they occur right away, or do they unfold at a later stage, after the network has already established itself?
For those seeking to understand the gender dynamics within their network, the option to color nodes based on sex becomes a key feature. This customization allows users to visualize and analyze the distribution of assistance across different genders, providing key insights into the nature of collaboration within the community.
LinkingGlass proves equally valuable when examining issues related to racial divides. In this context, nodes can be colored based on race, offering a lens through which to assess the distribution of help within diverse racial groups. Whether your focus lies in gender-based or race-based analysis, LinkingGlass empowers users with a visual representation of their network, facilitating a nuanced understanding of collaboration dynamics.
In essence, LinkingGlass not only uncovers the who, what, and when of assistance within your network but also offers a tailored approach to exploring the intricacies of diversity and inclusion. Whether you're navigating gender-specific inquiries or probing into racial dynamics, LinkingGlass provides a comprehensive and visually intuitive tool to enhance your understanding of patterns within your network.
Author, Emily Taylor Penix