Liberation Geographies and Education: Spatializing Liberatory Futures Through Black and Dalit/Caste-Oppressed Solidarity Building
Bibliographic record
Abstract
This project explored how public education in Canada can be imagined through solidarity building between Black and Dalit/Caste-oppressed youth. ‘Dalit’ is a name reclaimed by Caste- oppressed folks or individuals excluded by the South Asian Caste system, and Caste in South Asia is a system of religiously codified exclusion not unlike anti-Black racism. Anti-Black racism is a rampant reality in Canadian public schools. When it comes to issues of Casteism, similar research has not been explored extensively, however, emergent research shows the ways in which Casteism is an area of concern in Canadian education. While Canadian educational institutions are beginning to place an emphasis on dismantling anti-Black racism and Casteism, there are no works to date that have focused on intersectional solidarity. This study argues by building solidarity networks between the victims of Casteism and racism, Canadian educational institutions will be able to create equitable learning spaces for both Black and Caste-oppressed students. The theoretical anchors of this work are grounded in radical Black studies, Black and Dalit liberation geographies, Land-based theories, and anti-colonialism. The methodology informing this work comes from Critical Participatory Inquiry. Fifteen Canadian youth worked in collaboration with Teacher Candidates to think about how education can respond to the needs of both Black and Dalit/Caste-Oppressed students. Their collective work led to the development of two educational projects to advance Black-Dalit/Caste-oppressed solidarities in education.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.049 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".