Applying Critical Race Theory to Explore Services Needs and Pathways to Inclusion for African, Caribbean, and Black Youth in the Greater Toronto Area
Bibliographic record
Abstract
Despite comprising a significant 8.7% of the Greater Toronto Area’s population and enriching the city’s cultural landscape (Statistics Canada, 2022), African, Caribbean, Black youths face a harsh reality of unemployment rates doubling the national and provincial average (City of Toronto, 2017). This underscores the urgent need to understand and address these systemic inequities. This research applies Critical Race Theory to investigate how systemic anti-Black racism shapes the well-being, service needs, and pathways to inclusion of African Caribbean Black youths in the Greater Toronto Area. Leveraging a virtual digital ethnography approach, this study analyzes secondary data to augment the voices of African, Caribbean, Black youths. The key themes that emerged from the analysis are centring African and Caribbean Black youth voices, dismantling institutional anti-Black racism, transformative change, Anti-oppressive approaches, and systemic anti-Black racism and its impact. This research aims to inform the development of anti-racist interventions and promote pathways to inclusion that aids in the dismantlement of systemic barriers which has the ability to empower ACB youth to thrive within the GTA.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".