Substantiation as a Multitier Process: The Results of a NIS-3 Analysis
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Bibliographic record
Abstract
BACKGROUND: Previous studies on child maltreatment reporting have focused mainly on one level of substantiation. This article analyzes factors influencing the multitiered substantiation process. METHOD: The 1993 Third National Incidence Study (NIS-3) data of substantiated and non-substantiated reported incidents (N=7,263) of maltreatment were analyzed. Substantiation was classified into three categories: unfounded, indicated, and founded. Independent variables included demographic characteristics, case-processing variables, and maltreatment characteristics. DATA ANALYSIS: Bivariate and multiple logistic regression (MLR) analyses were calculated to determine whether demographic and case processing variables predicted unfounded or founded/indicated dispositions. Second-level analysis examined demographic, case processing, and maltreatment characteristics as predictors of founded or indicated status. RESULTS: These results showed that 60.2% of CPS investigations conducted were evaluated as unfounded, about 22% were categorized as founded, and 17% were classified as indicated. In the MLR analysis for the first level of substantiation, case processing variables were highly significant predictors of founded/indicated status. In the second-level substantiation MLR model, cases in the mid-range income level (dollars 15,000-29,999) had a lower probability (adjusted OR = .58, p = .02) of being founded than those of less than dollars 15,000, and reports involving Hispanic children (OR = 3.04, p = .05) were more likely than the "all other" race-ethnic social classification to have been substantiated as founded. CONCLUSIONS: This analysis of NIS-3 data suggests that a three-tiered rather than a two-tiered system is a more accurate representation of the CPS substantiation process. Further analysis of substantiation patterns is required to provide a basis for developing more effective investigation systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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