Breaking the Silence: The Process of Becoming Black in Schooling Through the Narratives of Expelled Black Males
Bibliographic record
Abstract
This study examined the nuanced process of becoming Black through the narratives of fifteen adult Black males, aged 23-29, who experienced expulsions from the Toronto District School Board (TDSB). Employing Critical Race Theory (CRT) and the concepts of whiteness as property, the study revealed how schooling perpetuates anti-Blackness, leading to systemic barriers and exclusions akin to prison-like environments. School expulsions echo themes of limited agency, voice, and segregation. Reviewing TDSB expulsion policies and participant narratives revealed pervasive anti-Blackness and systemic barriers aggravating school exclusions. Classroom exclusions often began early, with data showing how ongoing disengagement and segregation led to absolute exclusion from schooling, with impacts resonating into adulthood. Affirmations of Blackness often occurred only post-schooling for the participants. Despite adversity of having experienced an expulsion, the participants' narratives also revealed moments of vibrancy and positive interactions, highlighting the richness of their lives beyond expulsion. Their stories compel education to intentionally address how staff actions and implementation of exclusion policy and procedures support the continual processes of anti-Blackness strengthening systemic anti-Black racism. This study calls to establish approaches that dismantle the privilege of whiteness in staff exclusionary actions towards Black students. Focusing on an anti-Black racism approach and Black Gaze approach in educational discourse, that includes staff learnings from Black student and parent voices, is immediately needed when reviewing, writing, and putting expulsion policies and procedures into action. Fostering collaborations with Black communities, and enhancing support services for expelled Black, particularly male, students is urgent. Refocusing narratives to celebrate Black brilliance that transcend anti-Black biases and stereotypes would result in more inclusive environments where all iterations of Blackness are embraced. Transformative shifts in educational practices are necessary to counter whiteness, eliminate systemic anti-Black biases, dismantle white hegemony, and amplify the voices of Black students. This ensures that all Black students, particularly Black males, can affirm their Blackness in school, rather than staff practices perpetuating processes of continually becoming Black through the lens of anti-Blackness.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".