Handling Whiteness with Kid Gloves: The Experiences of White Diversity, Equity, and Inclusion Cocurricular Educators
Bibliographic record
Abstract
This critical narrative inquiry study examined how whiteness functioned in cocurricular diversity, equity, and inclusion (DEI) education by exploring the experiences of white DEI educators who facilitate these learning opportunities in their campus communities. Although there has been a focus on white educators who facilitate conversations in the classroom about race, racism, and antiracism, research on the experiences and applications of white educators who lead these conversations outside the classroom has remained insufficient. There also have been a number of studies on higher education practitioners that have engaged whiteness, but these analyses typically have examined the relationship between students and student affairs practitioners. By focusing on the experiences of white DEI educators, this research study sought to address empirical gaps in the literature on whiteness, white educators, and DEI work(ers). Informed by the work of four critical race and whiteness theorists, I used a thinking with theory approach to unearth how technologies of whiteness operated through participants’ experiences. Fifteen white DEI educators located at historically and/or predominantly white institutions from across the country participated in the study. Data collection included three interviews with each participant conducted between October 2023 and February 2024 and two optional focus groups with participants conducted in May 2024. Findings highlighted how participants understood and described the function of DEI education as a corrective whiteness project focused on addressing learner harm and institutional image management. In response to the research questions guiding the study, participants’ narratives illuminated the role of racial anxieties in their work; the social rules around embodiment, emotion, and discourse informing their praxis; and the challenges of being answerable and accountable in ways that did not reify participants’ white exceptionalism. The study’s implications provided considerations for DEI educators to better attend to how whiteness informs and influences their praxis and the overall project of DEI education. Although these implications may be relevant to all practitioners and researchers engaged in the DEI enterprise, these implications have utility for white DEI educators and educational researchers who study whiteness and white people. The final assertion for the study invites those invested in the DEI enterprise to reconsider and reconceptualize their commitments to DEI in education by interrogating technologies of whiteness in their work.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.016 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".