Systemic Abuse of Black Educational Leaders in K-12 Schools
Bibliographic record
Abstract
Anti-Black racism (ABR) is race-based discrimination experienced by Black educational leaders in the Ontario education system. Since the police officers murdered George Floyd, it has ignited the global Black Lives Matters movement. This has translated into K-12 Ontario schools with a more pronounced emphasis on addressing ABR and systemic racism to provide an environment that is rooted in equity. Despite this global action and ministry requirement, ABR continues to prevail and affects Black educational leaders’ ability to lead effectively. Black educational leaders are navigating a system that is unjust and rooted in colonialism which puts them at a disadvantage. Critical Race Theory lens is used in this Organizational Improvement Plan (OIP) to analyze the ability of Black educational leaders’ ability to lead effectively while simultaneously dealing with race-based aggressions consistently. Often, Black educational leaders must address stereotypes aimed at them due to the colour of their skin. This is an added burden that Black educational leaders encounter regularly. The problem is an action or comment may appear innocent; however, the impact is damaging towards Black educational leaders. These daily aggressions disrupt Black educational leaders’ ability to lead as well as cause additional unnecessary stress and adversity beyond the daily job requirements. This OIP outlines a change implementation plan using transformative and servant leadership approaches to help mitigate some of the ABR interactions experienced by Black educational leaders to create a workplace founded in equity and dignity. It also provides a platform for Black educational leaders to congregate and self-advocate safely. \nKeywords: anti-Black racism (ABR), Black educational leaders, aggressions, Critical Race Theory (CRT), systemic racism, equity
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".