Black Liberatory Education in Canada
Bibliographic record
Abstract
Purpose The purpose of this research is to acknowledge that while there has always been anti-Black oppression and state sanctioned violence towards Black children in schools, there have also always existed spaces where Black children in the care of Black parents and community members are encouraged to demonstrate brilliance and creativity. I am interested in documenting these spaces of resistance and freedom, and in understanding what lessons may be learned from the documentation and archiving of these spaces. The purpose of this work is to amplify the voices of Black community and to highlight the work of Black community spaces. Problem What are the tenets that make Black community led educational spaces liberatory and affirming for Black parents and children? Basic Design Black parents, youth, and community educators identified community minded educational spaces where they feel affirmed and liberated. I then interviewed Black educators, youth, and parents to understand the pedagogy used in these spaces. Interviews took between 20 minutes and one hour. I then coded the data in order to draw out re-emergent tenets among the community spaces featured in this research. Based on this coding I pulled out 7 tenets of Black liberatory education employed in Ontario. Findings This research explores 7 tenets of Black liberatory education that were commonly featured in Black community led educational spaces in Ontario: 1. Transformative Justice 2. Black affirmative curriculum 3. Intersectional curriculum/Disabilities Justice 4. Family building` 5. Arts based curriculum 6. Land Based Learning/Spirituality 7. Community Self-Determination
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".