IMAGINING WHITE VICTIMS AND PUNISHING BLACK TRAUMA: WHITENESS, ANTI-BLACKNESS, TRAUMA, AND SOCIAL WORK IN CANADIAN K-12 SCHOOLS
Bibliographic record
Abstract
This study critically examined how whiteness and anti-Blackness structure Canadian school social workers’ understanding and responses to students’ expressions of trauma in K-12 schools. Very few studies have previously explored the damage that increasingly popular trauma-engaged school programs may be doing, particularly to Black students. Therefore, this study addressed the role that school social workers play in reproducing harm for racialized, and especially Black, students when implementing trauma-engaged practices.\nThis critical qualitative study used an innovative methodology combining fictional vignettes and semi-structured interview questions with Ontario school social workers (n=19). Data were analyzed using critical whiteness, anti-Blackness, and critical trauma theories through Lawless and Chen’s (2018) critical thematic analysis. To address the relational accountability of white researchers during data collection, this study conceptualized an anti-racist research praxis, including the creation of an arts-based self-reflexive memoing strategy, the intentional challenging of racism from white participants, and a critical skepticism of self-as-researcher.\nThe findings from this study demonstrated how whiteness and anti-Blackness are enacted by school social workers doing trauma-engaged work. Participants frequently favoured a whitewashed and individualized definition of trauma, which excluded the racist and colonial roots of many forms of trauma and deepened the conceptualization of trauma as a form of white property. Despite claims to be “evidence-based practices,” many of the interventions followed the logics of whiteness, including: diagnostic differences and pathologization of racialized students, neoliberal manualization of care that values self-responsibilization and behavioural modification, and liability-centered decision making. Participants also imagined supporting ‘good,’ white trauma victims in the vignettes, moving them into systems of support while ‘bad’ racialized students were pushed into systems of punishment. Schools were often depicted as places of trauma for Black students, who were simultaneously invisibilized or under-protected as victims of trauma (both episodic and systemic) and hyper-visibilized for punitive and carceral responses. Despite participating in these harms, the findings also indicated that some social workers resist these ideologies and practices through individual interventions and advocating for systemic change. The study concludes with implications for theory, practice, policy, research, and social work education.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.066 | 0.039 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.005 |
| 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".