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Record W4319771326 · doi:10.31234/osf.io/z5dqf

The developmental interplay between household chaos and educational achievement from age 9 through 16 years: A genetically sensitive study

2023· preprint· en· W4319771326 on OpenAlexaff
Sophie von Stumm, Alexandra Starr, Iván Voronin, Margherita Malanchini

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicCognitive Abilities and Testing
Canadian institutionsUniversité Laval
FundersMedical Research CouncilEuropean CommissionNational Institutes of HealthBritish AcademyJacobs FoundationNuffield Foundation
KeywordsSocioeconomic statusDevelopmental psychologyPsychologyEducational attainmentInequalityConfoundingCHAOS (operating system)Demographic economicsDemographyEconomicsSociologyStatisticsMathematicsEconomic growthPopulationComputer science

Abstract

fetched live from OpenAlex

We tested whether associations between household chaos, which refers to confusion and disorganisation in family homes, and educational achievement are confounded by genetics and family socioeconomic status (SES). We modelled the developmental interplay between chaos and achievement, including their reverse association (i.e., achievement chaos), and its aetiology in up to 7,591 twin pairs (49% female), who were born in the mid-90s in the UK and assessed at age 9, 12, and 16 years. Associations between household chaos and educational achievement were consistently negative, bidirectional, and of small effect sizes across ages. These associations were best explained by genetic and environmental confounding. Family SES accounted for most of the confounding in the predictions from achievement to chaos; for the reverse, environments shared within families but distinct from SES were implied. Our findings suggest that long-term associations between children’s experiences of household chaos and educational achievement are modest and non-causal.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.001
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 head0.107
GPT teacher head0.370
Teacher spread0.262 · 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 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

Citations1
Published2023
Admission routes1
Has abstractyes

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