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Record W2102109040 · doi:10.1177/144078301128756346

Educational attainment of the children of divorce: Australia, 1940–90

2001· article· en· W2102109040 on OpenAlexaff
Mdr Evans, Jonathan Kelley, Richard A. Wanner

Bibliographic record

VenueJournal of sociology · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDisadvantageEducational attainmentOrdinary least squaresLogistic regressionConfoundingInstrumental variableSociologyRegression analysisDemographic economicsDemographyPolitical scienceEconomicsEconomic growthEconometricsLawStatistics

Abstract

fetched live from OpenAlex

Much research suggests that the disruption of marriage through parental death or divorce imposes a small but significant educational disadvantage on American children, although the most recent and comprehensive analysis casts serious doubt on this claim. What is the situation in Australia? Using representative national samples (n= 29,443) and OLS and logistic regression with robust standard errors, we estimate models controlling many potentially confounding variables. We find that divorce in Australia costs seven-tenths of a year of education, mainly by reducing secondary school completion. Importantly, divorce has become more damaging in recent cohorts.

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)0.001
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.362
Threshold uncertainty score0.720

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.348
Teacher spread0.311 · 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

Citations43
Published2001
Admission routes1
Has abstractyes

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