Guidelines for Using Child Welfare Administrative Data from a Measurement Perspective
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Bibliographic record
Abstract
Administrative data, or data routinely collected over the course of an agency’s programmatic activities (Yampolskaya, 2018), have enjoyed a surge in popularity among social science researchers in the past few years. This is no less true in child welfare, where administrative data offer a comprehensive, longitudinal, population-level source of information from which to identify risk and protective factors and analyze outcomes that are not subject to attrition, social desirability bias, or underestimation in self-reporting from parents (Brownell & Jutte, 2013). Given the potential benefits of administrative data, the purpose of this note is to describe some guidelines for using administrative data in child welfare research. The guidelines described in this note are grounded in measurement theory as well as lessons we learned from conducting research using administrative data and pertain to the type of research that administrative data are used for (that is, tracking research to...
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it