Substantiation as a Multitier Process: The Results of a NIS-3 Analysis
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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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 | 0.031 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".