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LESSONS LEARNED FROM A CHILD PROTECTION MEDIATION PROGRAM: If At First You Succeed and Then You Don't…

2003· article· en· W2018000587 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueFamily Court Review · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicChild Welfare and Adoption
Canadian institutionsnot available
Fundersnot available
KeywordsMediationThrivingPolitical sciencePsychologyFamily mediationChild protectionPublic relationsLawAlternative dispute resolution

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.059
GPT teacher head0.323
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it