Embodied experiences of race-based traumatic stress and the journey towards black joy
Bibliographic record
Abstract
This paper presents an embodied framework of Race-Based Traumatic Stress (RBTS) grounded in the lived experience of a Black African immigrant woman navigating structural racism, microaggressions, and cultural displacement in Canadian academic and professional spaces. Drawing on African feminist epistemologies, polyvagal theory, and African-centered decolonial thought, it conceptualizes RBTS as a culturally situated, embodied response to chronic racial harm. Through narrative reflection and Shona proverbs, the paper explores the emotional and physiological toll of racism while illuminating the protective role of cultural identity, ancestral wisdom, and community. The framework traces recursive phases of RBTS, beginning with migration hope and internalization, followed by overperformance, leadership challenges, inclusive exclusion, emotional labour, and psychological overload, and culminating in a reawakening through Black joy. This concept is introduced not as a fleeting emotion but as a radical, intentional, and culturally grounded practice of healing and resistance. It affirms Africentric knowledge, relational care, and collective affirmation as vital tools for reclaiming wellness. This contribution deepens global understandings of oppression-based trauma by centering embodied knowledge and advocating for culturally affirming mental health supports, community-led healing spaces, and systemic transformation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.012 |
| 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".