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Record W4417147456 · doi:10.1007/s44192-025-00352-w

Embodied experiences of race-based traumatic stress and the journey towards black joy

2025· article· en· W4417147456 on OpenAlexaffabout
Florence Kurai Mudzongo

Bibliographic record

VenueDiscover Mental Health · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicRacial and Ethnic Identity Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEmbodied cognitionRacismNarrativeHistorical traumaMental healthLived experienceGrounded theoryCulture theory

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.420
Teacher spread0.375 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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