The socio-technical organization of equity, diversity, and inclusion (EDI) in child welfare
Bibliographic record
Abstract
In this article we use institutional ethnography to investigate the intersection between provincially mandated digital technologies intended to streamline decision-making, improve communication, and create efficiencies and the development and implementation of strategies to institutionalize Equity, Diversity, and Inclusion (or EDI) in child welfare contexts. These two moves collide in the Child Welfare Redesign – a state-led initiative that organizes the convergence of data and diversity work in local Children’s Aid Societies in the Canadian province of Ontario. Drawing on 38 interviews with child welfare directors, supervisors, and managers coupled with extensive documentary research, our findings show how the Ontario Child Welfare Redesign goal to make child welfare services more “inclusive” and “culturally appropriate” for Black, Indigenous, and LGBTQ+ communities who are overrepresented among those receiving child welfare services is undermined by the continued use of provincial information management infrastructure and the provincially mandated Child Protection Standards and Tools.
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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 | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.088 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".