An intersectional narrative inquiry into why Black and racialized teachers quit the profession
Bibliographic record
Abstract
In this article, we report findings from a narrative inquiry into why Black and racialized teachers quit the profession. Using intersectionality as a theoretical framework, five in-depth narrative interviews were analysed, yielding six thematic threads: emotional labour and cultural taxation, subtle and overt systemic racial violence, technology-facilitated harassment, a tension between identity and professionalism, and disillusionment with school leadership and institutional inaction in addressing harm. The findings reveal the compounded challenges Black and racialized teachers face, when professional demands intersect with systemic marginalization, particularly at the intersections of race and gender. They also show that while institutional shortcomings impact all teachers, contemporary challenges such as school underfunding and staff burnout are multiplicatively exacerbated for Black and racialized teachers as structural educational inequities intersect with systemic violence. Findings point to the need for robust and intersectional retention strategies. Schools should do the courageous work of confronting the structural and cultural harms that make teaching untenable for many, reimagining educational spaces as ones that value, support, and sustain racialized educators. Without this shift, retention initiatives will remain incomplete and unable to address the deeper inequities at play.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".