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Record W2346749749

How Can We Best Listen to Children in Family Law Proceedings

2013· article· en· W2346749749 on OpenAlexaboutno aff
Michelle Fernando

Bibliographic record

VenueSSRN Electronic Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsAdversarial systemBest interestsFamily lawBest practiceVariety (cybernetics)Context (archaeology)Political scienceLawFamily courtQuality (philosophy)Common lawChild custodyRepresentation (politics)Public relationsSociologyPoliticsHistory
DOInot available

Abstract

fetched live from OpenAlex

This article critiques how children's views are commonly heard in adversarial family law proceedings, focusing on practices in New Zealand, Australia, Canada and the United Kingdom. Children's views play an important role in determining their best interests. How those views are heard impacts on the quality of evidence available to the court and on children's experiences. Benefits and limitations of expert reports, direct and best interests representation by lawyers and judicial meetings with children are discussed. These are viewed in the context of children's right to be heard and literature on children's dissatisfaction with their participation experiences. It is argued that New Zealand's team approach, where a child's views are heard through a variety of methods, is desirable. However, we must ensure not to expose children to proceedings in ways that may be contrary to their best interests.

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 imitation

Not 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.

metaresearch head score (Codex)0.022
metaresearch head score (Gemma)0.085
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.085
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0120.024
Scholarly communication0.0130.018
Open science0.0020.011
Research integrity0.0100.017
Insufficient payload (model declined to judge)0.0070.003

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 head0.016
GPT teacher head0.290
Teacher spread0.274 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2013
Admission routes1
Has abstractyes

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