Building School Leaders' Capacity to Challenge Anti-Black Racism in Schools
Bibliographic record
Abstract
This Organizational Improvement Plan (OIP) was developed based on a problem of practice (PoP) in the Green District School Board (GDSB) related to equity leadership, and building the capacity and efficacy of school leaders to address anti-Black racism in K-12 schools. Despite efforts to create more equitable and inclusive classrooms, Ontario schools continue to display achievement gaps and negative outcomes for Black students, including streaming into courses below their abilities, harsher discipline, and higher push out and suspension rates compared to that of their peers. Feeling of isolation, lack of engagement and teacher connection further exacerbate the racial trauma and the negative experiences of Black students. This work is undertaken during a global pandemic that has further exposed the depth of societal inequities, and the growing demand for action and accountability to correct the prevailing racial injustices impacting Black students. Using a critical race theory (CRT) lens, the OIP outlines a change implementation plan that looks at key structures, learning approaches, and accountability measures that center the voices and perspectives of Black students and their families in order to break down and dismantle systemic barriers and address interpersonal racism and discrimination in schools. Social justice, culturally responsive, and distributive leadership are key leadership approaches to disrupt the status quo and create inclusive spaces. A hybrid version of Lopez’s NOFS, Kotter’s XLR8, and Deming’s PDSA models are used to stop and name anti-Black racism, and structure the necessary learning and supports for school leaders to authentically engage with the Black community and co-create intentional actions that transcend into tangibly different experiences and outcomes, within a culturally responsive school environment.
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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".