Child Welfare Outcome Indicator Matrix
Bibliographic record
Abstract
Child welfare practice is at a turning point in Canada. Inquests and media interest have drawn public attention to the plight of maltreated children. With this increased attention there is a risk that the complexity inherent in helping maltreated children and their families may not be fully recognized. The proposed multi-dimensional ecological framework reflects the complex balance child welfare service providers seek to maintain between a child’s immediate need for protection, a child’s long-term needs for a nurturing and stable home, the family’s potential for growth and the community’s capacity to meet a child’s needs. 1,2 The outcome measurement strategy presented in this document proposes measuring child welfare outcomes in four domains that reflect the broad ecological traditions of Canadian child welfare practice: child safety, child well-being, permanence, and family and community support. The indicators selected for tracking outcomes are simple, can be feasibly documented with minimum introduction of new instruments, and are meaningful for front-line workers, managers, policy makers and the general public. While most of these indicators taken individually are only proxy measures of child and family outcomes, as a set of ten indicators they provide a broad perspective on the children served by the child welfare system and some outcomes of that service. Ecological Outcome Framework
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.008 |
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".