Challenging color-evasion in democratic dialogue: using critical race discourse analysis to generate practical theory for facilitators*

ABSTRACT

Guided by Critical Race Discourse Analysis and Grounded Practical Theory this paper analyzes interaction during classroom dialogs to examine how democratic inclusion is undermined by color-evasion. Too often color-evasion as a discursive technique perpetuates exclusion, inhibits democratic values under the guise of inclusion, and consequently, protects whiteness and puts undue burden on non-white participants. Thus, we argue that democratic facilitators who are concerned with inclusion should listen for and disrupt color-evasiveness. We offer a practical theory to guide facilitators recognizing and challenging color-evasion through questions that encourage the group to contend with why evading talk about race undermines democratic dialogue.

KEYWORDS:

• Color-evasiveness
• color-evasion
• democratic inclusion
• democratic deliberation
• democratic dialogs
• practical theory

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 We use non-white to describe racially marginalized folks that have been historically impacted by racism and white supremacy. Aware of the ways that ‘non-white’ can be interpreted as a way to ‘define groups of people in terms of what they are not’ (Tatum, 2017, p. 95), we use the term as a discursive practice that names and exposes whiteness and white supremacy in a context where both are deliberately rendered invisible. Furthermore, because ‘people of color’ implies the colorless-ness, and thus raceless-ness of whiteness, using non-white is one way to interrupt that line of thinking. We use ‘people of color’ when citing a scholar that uses that term.

2 When non-white facilitators decide not to challenge color-evasion we acknowledge that practice may emerge as a form of emotional and psychological protection due to the racial fatigue experienced in these conversations.

Additional information

Funding

Data collection for this project was funded by the Spencer Foundation Award #201600016.