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Record W2768686550 · doi:10.5204/thesis.eprints.113831

Family Law Property Settlements: Principled Law Reform for Separated Families

2017· dissertation· en· W2768686550 on OpenAlexfundno aff
C J Turnbull

Bibliographic record

VenueQueensland University of Technology · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsnot available
FundersUniversity of California, Los AngelesBC Cancer AgencyAttorney-General's Department, Australian GovernmentUniversity of CanberraUniversity of New South WalesUniversity of MelbourneFordham UniversityJohns Hopkins UniversityAustralian GovernmentPrinceton University
KeywordsFamily lawDisadvantageConsistency (knowledge bases)Property (philosophy)Context (archaeology)Human settlementEconomic JusticeLawProperty lawSettlement (finance)Law and economicsSociologyPolitical scienceEconomicsGeographyProperty rightsMathematicsFinance

Abstract

fetched live from OpenAlex

This thesis investigates the philosophical basis, values, and practical application of family law, specifically property settlements for separated spouses, where those spouses have children of their relationship. It is a step forward in understanding of how judges decide cases, as it reports on the results and process of decision-making using 200 decisions from family law courts. It develops criteria for defining justice in this context, including a clear purpose to the law, consistency of decision-making, non-discrimination between spouses, giving weight to financial disadvantage, and priority to the economic interests of children.

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)0.024
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.024
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.036
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0070.023
Scholarly communication0.0080.009
Open science0.0020.007
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.286
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations1
Published2017
Admission routes1
Has abstractyes

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