The Manifest and Latent Functions of Differential Response in Child Welfare
Bibliographic record
Abstract
Although previous research has explored the efficacy of differential response (DR) programs in child welfare, there have been no studies to date about coding decisions between designations by child protection service agencies. Research has explored client satisfaction with DR as well as rates of recidivism and removal/placement but with limited attention paid to the rationales behind coding decisions and recoding, once an initial designation pathway is assigned. This descriptive study uses data previously gathered by child protection social workers to qualitatively evaluate the fidelity of implementation of family development response (FDR) in British Columbia and the integrity of the program with regard to its stated objectives. Based on a random sample of intakes, decision-making fidelity to code as FDR or investigation (INV) was examined by exploring rationales behind coding at critical decision points and mechanisms for recoding during family involvement with child protective services. Subsequently, this study examined whether cases that had been coded as FDR differed substantially from INVs in terms of service provision, outcomes, and appropriateness of FDR for high-risk cases.
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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 | 0.037 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".