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Record W4282000494 · doi:10.1093/publius/pjac014

Regulating Family Law in Federations: The Impact of De/Centralization, Religion, and International Treaties on Abortion and Child Marriage Policies

2022· article· en· W4282000494 on OpenAlexaff
Jill Vickers

Bibliographic record

VenuePublius The Journal of Federalism · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsCarleton University
Fundersnot available
KeywordsAbortionInheritance (genetic algorithm)LegislationFederalismFamily lawAffect (linguistics)LawPolitical scienceRace (biology)SociologyLaw and economicsGender studiesPregnancy

Abstract

fetched live from OpenAlex

Abstract There has been little study of the effects of federalism on the regulation and reform of family law (FL). This article addresses that gap by identifying variables that affect two aspects of FL: child marriage and abortion. The underlying research question is what conditions make it easier to reform discriminatory FL in one federation compared to another. FL involves multiple rules governing marriage, divorce, custody, adoption, and often inheritance and family property use. Many gender scholars also include reproductive self-determination, specifically abortion. FL rules also have multiple elements: e.g., marriage regulations govern age, “race,” sex, domicile, etc., that often differ across constituent units. The main variable examined is the extent of centralization, because the dominant pattern of regulation in these cases is centralized legislation and decentralized implementation. The other two variables explored are religion and international norms-building and their effects on FL regulation and reform.

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 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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.986

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 head0.025
GPT teacher head0.330
Teacher spread0.305 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2022
Admission routes1
Has abstractyes

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