LESSONS LEARNED FROM A CHILD PROTECTION MEDIATION PROGRAM: If At First You Succeed and Then You Don't…
Bibliographic record
Abstract
This article discusses the U.A.L.R. child protection mediation program as well as several other child protection mediation programs in order to examine what makes a program a continuing success. Child protection mediation programs have gone through a period of tremendous progress and growth over the past 20 years in the United States and Canada. Numerous studies have shown that child protection mediation helps families and courts by lowering the amount of time that children spend in foster care and the amount of costs for courts and agencies. Child protection mediation is an essential tool for juvenile courts and the families that have cases there. This article addresses the development of child protection mediation programs, their importance to juvenile courts, and some reasons that these programs succeed or fail. Although many of these programs have early accomplishments, they have not always been able to maintain their growth or to continue to exist. The U.A.L.R. Mediation Project has not sustained its early levels of cases or referrals from court for numerous reasons. Using the techniques of other thriving programs, we will attempt to restart and re‐energize the program. It has been established that the people who have a role in the establishment of a program, the funding sources and especially the commitment of the parties to the program all have a significant long‐term impact. This article points out how programs should begin and proceed if they are to be a long‐term success.
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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".