Examining the effects of antidiscrimination laws on children in the foster care and adoption systems
Bibliographic record
Abstract
Abstract How are children affected when states prohibit child welfare agencies from discriminating against same‐sex couples who wish to foster or adopt? This question stands at the heart of a debate between governments that seek to impose such antidiscrimination requirements and child welfare agencies that challenge them on religious freedom grounds. Yet until now there has been no reliable evidence on whether and how antidiscrimination rules for these agencies impact children. We have conducted the first nationwide study of how child outcomes vary when states adopt such antidiscrimination rules for child welfare agencies. Analyzing 20 years of child welfare data (2000–2019), we estimate that state antidiscrimination rules both (1) modestly increase children's success at finding foster and permanent homes, and (2) greatly reduce the average time to place children in such homes. These effects vary among subgroups, such that children who are most likely to find a home are generally not affected by state antidiscrimination requirements, whereas children who are least likely to find a home (primarily older children and children with various disabilities) benefit substantially from antidiscrimination measures. We estimate that the effect of antidiscrimination rules is equivalent to 15,525 additional children finding permanent homes and 360,000 additional children finding foster homes, nationwide, over a period of 20 years. Overall, the project offers two key contributions: First, it provides empirical grounding for some of the most heated constitutional and political battles of the culture wars. Second, it advances empirical legal studies by bringing machine learning causal inference to law.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".