Equity Without Evidence? Analysis of the City of Toronto’s Action Plan to Confront Anti-Black Racism
Bibliographic record
Abstract
This study examines the extent to which the “Job Opportunities & Income Supports” section of the City of Toronto’s Action Plan to Confront Anti-Black Racism defines and measures progress in improving employment and economic opportunities outcomes for Black Torontonians. Using 38 City reports and documents from 2017 to 2024, the study uses an evaluative, qualitative analysis supported by NVivo coding to evaluate clarity, transparency, and empirical verifiability. The study finds that while Toronto’s Action Plan reflects institutional commitment and public accountability, it lacks standardized evaluation frameworks and measurable outcome indicators necessary for verifying long-term impact. Although this reflects broader gaps in municipal DEI practices, it does not suggest a lack of progress. Instead, it highlights the evolving nature of equity work and the need for stronger tools to track and sustain change. Recommendations include developing standardized outcome indicators, enhancing data transparency, and utilizing standardized evaluation approaches to more effectively measure the impacts of equity over time.
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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 | 0.020 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".