Search results

Racial stigma and racial criminalization have been centralizing pillars of the construction of Blackness in the United States. Taking such systemic injustice and racism as a given, then question then becomes how these macro-level arrangements are reflected in micro-level processes. This work uses radical interactionism and stigma theory to explore the potential implications for racialized identity construction and the development of “criminalized subjectivity” among Black undergraduate students at a predominately white university in the Midwest. I use semistructured interviews to explore the implications of racial stigma and criminalization on micro-level identity construction and how understandings of these issues can change across space and over the course of one's life. Findings demonstrate that Black university students are keenly aware of this particular stigma and its consequences in increasingly complex ways from the time they are school-aged children. They were aware of this stigma as a social fact but did not internalize it as a true reflection of themselves; said internalization was thwarted through strong self-concept and racial socialization. This increasingly complex awareness is also informed by an intersectional lens for some interviewees. I argue not only that the concept of stigma must be explicitly placed within these larger systems but also that understanding and identity-building are both rooted in ever-evolving processes of interaction and meaning-making. This research contributes to scholarship that applies a critical lens to Goffmanian stigma rooted in Black sociology and criminology and from the perspectives of the stigmatized themselves.

Purpose: The study aims to analyse and discuss the effect of COVID-19 on businesses. The chapter discusses the various machine learning (ML) tools and techniques, which can help in better decision making by businesses in the present world.

Need for the Study: COVID-19 has increased the role of VUCA elements in the business environment, and there is a need to address the challenges faced by businesses in such environment. ML and artificial learning can help businesses in facing such challenges.

Methodology: The focus and approach of the chapter are in the context of using artificial intelligence (AI) and ML techniques for decision making during the COVID-19 pandemic in a VUCA business environment.

Findings: The key findings and their implications emphasise the importance of understanding and implementing AI and ML techniques in business strategies during times of crisis.

Practical Implications: The chapter’s content is in the context of using AI and ML techniques during the COVID-19 pandemic and in a VUCA business environment.