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This article provides an overviews of the concept of racelighting. Racelighting is “is an act of psychological manipulation where Black, Indigenous, and People of Color (BIPOC) receive racial messages that lead them to second-guess their lived experiences with racism”

This conceptual paper articulates four primary ways that racelighting manifests in the lives and experiences of Black, Indigenous and People of Color (BIPOC).

There are four common messages that often lead to racelighting: stereotype advancement, resistive actions, inauthentic allyship and misrepresenting the past.

While much has been written about gaslighting, few frameworks articulate how gaslighting occurs in a racialized context.

Psychotherapy and clinical supervision outcomes are influenced by client and supervisee factors, one of which is cultural identity. Those with diverse racial and ethnic minoritised identities often experience disparities in therapy outcomes. Therapists and supervisors need to be responsive to the identity of those they support. The multicultural orientation (MCO) framework is an emerging concept in psychotherapy and clinical supervision that may offer these practitioners a framework to be responsive.

A preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews was conducted. Six databases, PubMed, Scopus, Embase, Academic Search Complete, Web of Science and PsychInfo, were searched for peer-reviewed literature published in English between the years 2000 and 2023.

A total of 1,553 sources were identified, of which (n = 42) are included in this review. Findings suggest that MCO is still in its infancy as applied to therapy and clinical supervision. Most of the research has been conducted in America, using quantitative methodologies with white western populations. Cultural humility is the most studied MCO pillar, and variables such as reductions in psychological stress, the working alliance and microaggressions are reported on as outcomes. MCO applied to the group therapy process is an emerging finding of interest. However, more research is needed, especially experiential designs across different and diverse populations and contexts.

MCO is an emerging therapy and clinical supervision process that has the potential to improve the outcomes for therapy clients and supervisees. Further research is needed to replicate current studies, and further research with diverse populations, countries and contexts should be undertaken as a priority.

This paper explores organization-level racialized work strategies for maintaining racialized organizations (Ray, 2019). It focuses on intentional actions to maintain dominant racial norms, demonstrating how work strategies are informed by dominant racial structures that maintain racial inequities.

We compiled a chronological case study (Yin, 2012) based on 168 news media articles and various organizational documents to examine responses to athlete protests at the University of Texas at Austin following the death of George Floyd. Gioia et al.’s (2013) method uncovered how dominant racial norms inform organizational behaviors.

The paper challenges institutional theory neutrality and identifies several racialized work strategies that organizations employ to maintain racialized norms and practices. The findings provide a framework for organizations to interrogate their strategies and their role in reproducing dominant racial norms and inequities.

In 2020, the Black Lives Matter (BLM) movement was reinvigorated within sporting and corporate domains. However, many organizations engaged in performativity, sparking criticism about meaningful change in organizational contexts. Our case study examines how one organization responded to athlete activists’ BLM-fueled demands, revealing specific racialized work strategies that maintain structures of racism. As organizations worldwide disrupt and discuss oppressive structures such as racism, we demonstrate how organizational leadership, while aware of policies and practices of racism, may choose not to act and actively maintain such structures.

Although risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.

To convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.

The PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

The relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.