Count Us In: Development and Insights From Ontario’s Equity and Inclusion Data Initiative in Social Work and Social Service Work Regulation
Bibliographic record
Abstract
This article describes the development, implementation and first-year findings of the Ontario College of Social Workers and Social Service Workers' Equity and Inclusion Data Initiative. This data project was developed to help identify and monitor systemic racism and discrimination within the professions of social work and social service work in Ontario. This initiative was based on the fundamental principle that only what is measured can be effectively understood and improved. College registrants were invited to share their demographic information on a voluntary basis. Data collection launched in the 2024 registration renewal period, with 66.5% response rate in its first year. This is an ongoing large-scale change management initiative, requiring strategic engagements with registrants, clients, government, staff, and other key engagement groups. This workforce project is an innovative example of how demographic data collection can advance equity, diversity, inclusion, and anti-racism efforts in provincial regulation, including healthcare.
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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 | 0.077 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".