Toward Evidence-Informed Policy and Practice in Child Welfare
Bibliographic record
Abstract
Drawing on the authors’ experience in the international Campbell Collaboration, this essay presents a principled and pragmatic approach to evidence-informed decisions about child welfare. This approach takes into account the growing body of empirical evidence on the reliability and validity of various methods of research synthesis. It also considers wide variations in the cultural, economic, and political contexts in which policy and practice decisions are made—and the contexts in which children live and die. This essay illustrates the use of Campbell and Cochrane systematic reviews to inform child welfare decisions in the diverse contexts that exist around the globe.
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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 | 0.615 | 0.584 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.013 | 0.095 |
| Scholarly communication | 0.053 | 0.038 |
| Open science | 0.015 | 0.042 |
| Research integrity | 0.046 | 0.043 |
| 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".