White Fragility: An Emotion Regulation Perspective
Bibliographic record
Abstract
To address anti-Black racism, systemic change across many domains in American life will be necessary. There are many barriers to change, however, and progress requires identifying these barriers and developing tools to overcome them. Given that White individuals disproportionately occupy ‘gatekeeping’ positions of power, one key barrier to systemic change is rooted in White individuals’ emotional (and emotion-regulatory) responses when considering their own role in racism (e.g., involvement in racist systems, biased actions). White people often experience such moments as a jeopardy to their valued goals and are consequently highly motivated to reduce the distress they feel by denying or avoiding the issue – a multi-faceted response known as a White fragility response. When White individuals enact a White fragility response, they can further damage the well-being of Black members of their community and weaken their own motivation for systemic change. Given its stark costs, it is critical to understand White fragility responses. In this article, we argue that White fragility can be usefully viewed through the lens of emotion and emotion regulation theory. In particular, we describe the emotion and emotion regulation responses that characterize White fragility, summarize the wide-ranging consequences of White fragility responses, highlight more sustainable ways forward, and end by considering a broader fragility framework that acknowledges multiple dimensions of power. Although emotion regulation lies at the heart of White fragility, emotion regulation is also a tool that can be leveraged for greater justice.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".