Sowing the Seeds of Hope: Keeping children safe with families
Bibliographic record
Abstract
Safe children. Strong families. Supportive communities. Casey Family Programs uses these words to describe the desired outcomes of our mission to provide, improve — and ultimately prevent the need for — foster care.Read them in reverse — supportive communities, strong families, safe children — and a framework emerges. If the first condition — supportive communities — is true, the next can follow. If the next is true — if families are strengthened by those supportive communities — then we can have safe children.Understanding that all three are connected is key to improving safety for our nation's children, guiding us to think, plan and act differently.Over the past quarter century, this country has made great progress in keeping children safe, with significantly fewer experiencing abuse or neglect. Much of that has taken place in recent decades, a time when communities across the nation have begun to think, plan and act based on data, research and experience of what works best to keep children safe in their own families and cultures. Local, state, tribal and federal leaders have worked to develop and implement policies and practices that ensure more children can grow up safely in strong families and supportive communities. From Texas to Washington state, Kansas to Connecticut, a shared vision is growing of a child and family well-being system that supports improved safety. In this report, we look at how communities are adapting their approach to ensuring child safety and what leaders from all sectors can do to support continued progress.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".