Forming Authentic and Purposeful Relationships with Racialized Communities from an Anti-Oppressive Lens: A Framework for African, Caribbean, and Black Communities
Bibliographic record
Abstract
In collaboration with London InterCommunity Health Centre this research focused on identifying priority areas for anti-Black racism interventions in London, Ontario. Semi-structured interviews were conducted with stakeholders from London’s African, Caribbean, and Black (ACB) communities. Interpretive description methodology guided analysis and interpretation. Participants indicated that anti-Black racism is ever-present in the community, with systemic racism leading to the most harm. Racism should be addressed by creating ACB-specific services and education for non-Black communities; and increased representation, inclusion, and engagement of ACB people within organizations, especially leadership. A framework to direct how organizations can develop authentic and purposeful relationships with ACB communities, and how this can be achieved in a “power-with” way, is presented to support the creation of improved and sustainable relationships with racialized communities in London, Ontario and beyond, thus contributing to health equity and social justice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".