A Critical Exploration of DEI Leadership Practices
Bibliographic record
Abstract
The primary goal of my research is to understand how practices of diversity, equity, and inclusion (DEI) are used to mitigate the elevated numbers of children of African heritage in child welfare. The disproportionate state-sanctioned child welfare apprehensions of Black children present as policing our most vulnerable members from communities of African heritage – our children. The anti-Black state violence in Ontario has been “acknowledged” by child welfare agencies who are now required to address the racial disparities within child welfare agencies. This thesis attempts to understand the histories, complexities, and current measures aimed at mitigating disparities of African, Caribbean, and Black children involved in child protective services from the perspective of child welfare service providers of African heritage. Diversity, Equity, and Inclusion (DEI) are incorporated into hegemonic child welfare institutions while the provincial government has failed to publicly critique the current measures implemented to address the disparities for communities of African heritage. Five participants were recruited from the Greater Toronto Hamilton area to participate in one-to-one interviews
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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 | 0.032 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.027 | 0.052 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.010 |
| 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".