An Idea whose Time has Come: Tracing the Emergence and Implementation of the Toronto Action Plan to Confront Anti-Black Racism from 2011-2021
Bibliographic record
Abstract
Structural racism refers to embedded processes in policies, laws and institutional practices that provide advantages to racial groups deemed as superior, while differentially disadvantaging or neglecting racial groups assigned as inferior. Anti-Black racism refers to a distinct form of racial harm rooted in the European colonization of Africans and legacies of the transatlantic slave trade. As a growing body of evidence is showing, racial discrimination has important implications for health because discriminatory practices produce avoidable and unjust health inequities and poor health outcomes. Less is known about the policy processes that influence the structural and social determinants of health, including racism, and whether and how policy agendas integrate health equity aims. This dissertation research analyzed the Toronto Action Plan to Confront Anti-Black Racism through a health equity lens – how anti-Black racism emerged as a policy issue for municipal government action in Toronto, what events contributed to policy development and implementation, the role of actors in shaping the agenda, and how the COVID-19 pandemic disrupted or galvanized focus on addressing anti-Black racism. Critical Race Theory (CRT), Public Health Critical Race Praxis (PHCRP) and John Kingdon’s Multiple Streams Framework (MSF) were applied in combination. Findings indicate that the City has a historically violent relationship with the Black community, and barriers to addressing anti-Black racism through policy implicates the ‘system’, and the actors operating within it and for it, because they remain committed to maintaining the White supremacist status quo and questioning why Black when operationalizing this municipal policy. I found that during the pandemic, neighbourhoods in Toronto with consistently higher rates of COVID-19 were also the most historically disadvantaged based on the analysis by the City that designates neighbourhood improvement areas. In the face of deficiencies in the established public health system, actors I characterize through this analysis as Black health champions stepped in to fill this gap through individual and collective action. Further research in Canada’s growing Black health research agenda should elaborate on the important health and social supports that community health centers (CHC) provide, and how race and gender intersect in public health policy processes. Implications from this study suggest a need for collaboration, dialogue, and documenting of promising practices across jurisdictions aiming to implement anti-racist policy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".