Anti-racist and Decolonial Educational Leadership in Ontario: An Intertwined Journey of Suffering, Joy and Radical Love
Bibliographic record
Abstract
Black, Indigenous and racialized educational leaders hold a pivotal role in establishing and protecting equitable and just spaces in Ontario’s K-12 education. This qualitative case study draws on their experiences as they navigate the neoliberal educational framework of Ontario as dedicated anti-racist and decolonial educational practitioners. By shedding light on and understanding the mental health impacts, joys and struggles of Black, Indigenous and racialized educational leaders, this study sought answers about how their personal wellbeing and mental health are impacted by their work. The study also inquired about ways they feel supported to heal and thrive as they continue to do anti-racist and decolonial work in education. The findings of this study reveal their joys and struggles to be intertwined experiences; the complex juxtapositions of emotions that stem from their work experiences, often frame their professional identities and fuel their work. With minimal to non-existence systemic protection and safety, adopting an anti-racist and decolonial praxis while existing as Black, Indigenous or racialized peoples come at detriments to their mental wellbeing and humanity. These dedicated and transformational educational leaders find inspiration and joy by supporting and advocating for students; they find healing and solace in affinity spaces they seek out and create; they find radical love and strength in their collective tears, laughter and work. Through their lived experiences, wisdom and recommendations, this study hopes to inspire systemic changes (i.e., policies, funding, programming) which will accommodate, not impede, the work of such courageous and dedicated leaders.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.037 | 0.016 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".