Measures such as ‘ethical AI’ and ‘good data’ will not bring about social justice, end racial capitalism or forestall climate disaster. How to channel discontent and counter-hegemony into an actual transfer of power in the late platform age?
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The current transnational climate (British Council, 2014) in Europe is likely to continue to generate institutional and classroom situations which dictate that difference and otherness be the norm rather than the exception. Unfortunately, in the 1960's, Black and minority ethnic (BME) migrants from the former British colonies had less-than-favorable educational experiences in Britain due to prejudice and stereotyping mainly arising from cultural differences. Since then there have been a plethora of studies, policies, and reports regarding the perpetuation of discrimination in educational institutions. Today, British higher educational institutions have finally begun to recognize the need to reduce progression and attainment gaps. However, their focus tends to only consider the student “Black and Minority Ethnic attainment gap” with almost no attention being given to educators', or more specifically there is a distinctive lack of thought given to the female BME educators' progression and attainment in British HEIs. As such, this paper draws theoretically and conceptually on critical cultural autoethnography, to illustrate the value of conducting research into a female's BME educators' personal and professional experiences, and “gives voice to previously silenced and marginalized experiences” (Boylorn and Orbe, 2014, p. 15). In doing so, I highlight how higher educational institutions underutilisation of such competencies and contributions have and continue to perpetuate BME underachievement. I conclude the paper by questioning the accountability of providing support for BME educators progression and attainment, challenge educational leaders to consider the value and utilization of cultural knowledge, and implore all educators to reflect on how their personal experiences influence their professional identity.
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In this paper, we present a digital tool named Diversity Perspectives in Social Media (DivPSM) which conducts automated content analysis of strategic diversity communication in organizational social media posts, using supervised machine-learning. DivPSM is trained to identify whether a post makes mention of diversity or a diversity-related issue, and to subsequently code for the presence of three diversity dimensions (cultural/ethnic/racial, gender, and LHGBTQ+ diversity) and three diversity perspectives (the moral, market, and innovation perspectives). In Study 1, we describe the training and validation of the instrument, and examine how it performs compared to human coders. Our findings confirm that DivPSM is sufficiently reliable for use in future research. In study 2, we illustrate the type of data that DivPSM generates, by analyzing the prevalence of strategic diversity communication in social media posts (n = 84,561) of large organizations in the Netherlands. Our results show that in this context gender diversity is most prevalent, followed by LHGBTQ+ and cultural/ethnic/racial diversity. Furthermore, gender diversity is often associated with the innovation perspective, whereas LHGBTQ+ diversity is more often associated with the moral perspective. Cultural/ethnic/racial diversity does not show strong associations with any of the perspectives. Theoretical implications and directions for future research are discussed at the end of the paper.
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