“A Rough Road to the Stars”: Exploring Resilience Through Overcoming the Trauma of Anti-Black Racism
Bibliographic record
Abstract
ABSTRACT Over the last decade, there has been growing body of research and scholarship exploring the experiences of anti-Black racism and its effects on Black Canadians. However, there seems to be little or no research that looks at the ways in which Black Canadians mediate the negative effects of anti-Black racism from a strengths-based perspective. Much of the literature is deficit-based and appears to address ways in which Black individuals survive but does not describe how they are able to thrive. The present study was conducted to explore how Black Canadians mediate the potential negative mental health effects (specifically racial stress or racial trauma) of anti-Black racism. A constructivist grounded theory methodology was used. Ten (10) Black Canadians in the GTA (Greater Toronto area, including Toronto) were interviewed on their experiences of anti-Black racism. Four core themes emerged from the interview data: awareness and understanding of anti-Black racism; experiences of anti-Black racist incidents; the effects of anti-Black racist experiences; and mediating the impacts of anti-black racism through resilience/resistance. The results of the study illustrated the nuanced, multi-layered, and diverse experiences of Black Canadians and how they navigate and make sense of ‘everyday racism’. This study posits that there needs to be a critical awareness of the relationship between white supremacy and anti-Blackness within the Canadian context if anti-Black racism is to be psychologically understood and engaged with. A mid-level theory illustrating the ways in which Black Canadians mediate the negative effects of anti-Black racist incidents arose from the data. Implications for theory, research and practice in counselling and psychotherapy are offered. Recommendations for a de-colonized, culturally specific, and trauma-informed therapeutic approach are also offered. The study’s limitations are discussed. Key words and Phrases: Anti-Black Racism, Anti-Black Racist Incidents, Racial Trauma, Mediate, Resilience/Resistance, Black Canadians, Race, Canada
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.017 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".