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 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 | 0.035 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.044 | 0.115 |
| Scholarly communication | 0.026 | 0.011 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".