Advancing Anti-Racism in Child Policy Advocacy
Bibliographic record
Abstract
Although child policy advocates support and protect children’s rights, research evidence does not indicate that these professionals and organizations have addressed embedded racial disparity and disproportionality in the child welfare system that renders children vulnerable in the first place. This article argues that adopting anti-racism is essential to child advocates committed to dismantling racist structures at the core of child welfare. Anti-racism enables child policy advocates to scrutinize and dismember the Eurocentric structures, biases, and practices that keep Black and Brown children and families entangled in the child welfare system. We provide background on child welfare and child policy advocacy. Next, we offer intentional anti-racist strategies for child policy advocates to disrupt the child welfare system. We conclude with recommendations for anti-racist practices to eliminate racial disparity and disproportionality in the child welfare system.
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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 | 0.132 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.035 | 0.078 |
| Scholarly communication | 0.025 | 0.017 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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".