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ASSESSMENTS FOR POSTSEPARATION PARENTING DISPUTES IN CANADA

2004· article· en· W2081683432 on OpenAlexaffabout
Nicholas Bala

Bibliographic record

VenueFamily Court Review · 2004
Typearticle
Languageen
FieldMedicine
TopicEthics and Legal Issues in Pediatric Healthcare
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsContext (archaeology)Mental healthFamily lawSupreme courtLawPsychologyBest interestsChild custodyPolitical scienceCriminal lawCommon lawOrder (exchange)PsychiatryBusiness

Abstract

fetched live from OpenAlex

There is controversy in Canada about the use of assessments by mental health professionals to assist in the resolution of postseparation disputes between parents about their children. Although the principles developed by the Supreme Court of Canada to govern the admission of expert evidence in criminal law cases provides guidance for judges in family law cases, in deciding whether to order an assessment or admit expert evidence, family law judges must also take account of the child‐related context. Mental health professionals can provide valuable information that would otherwise be unavailable when making prospective decisions about children. Court‐appointed assessors also have a significant institutional role in the family law cases that has no equivalent in the criminal law context. Assessors are important not only for the relatively rare cases that go to trial, but they also play a central role in helping to resolve the much larger number of cases that are settled.

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)0.008
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.515

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.042
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.004
Science and technology studies0.0100.002
Scholarly communication0.0030.001
Open science0.0030.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.065
GPT teacher head0.423
Teacher spread0.358 · 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 designObservational
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

Citations16
Published2004
Admission routes2
Has abstractyes

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