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Record W2982591148

Family structure and child cognitive outcomes: Evidence from Canadian longitudinal data

2018· preprint· en· W2982591148 on OpenAlexaboutno aff
Ana Ferrer, Yazhuo Pan

Bibliographic record

VenueEconstor (Econstor) · 2018
Typepreprint
Languageen
FieldSocial Sciences
TopicIntergenerational and Educational Inequality Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReading (process)RemarriageCognitionLongitudinal studyDevelopmental psychologyAssociation (psychology)PsychologyMedicineSociologyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This study investigates the role of family structure on cognitive outcomes of children. Using the rich panel data information from the National Longitudinal Survey of Children and Youth (NLSCY), collected on children and their families biennially since 1994, we investigate the association between a child's math & reading performance and family structure and changes in family structure. We find that children who stay-in or move-to non-intact families have lower reading scores than those who stay in intact families. Although initial findings indicate that family structure appears to have overall little effect on children's math performance, analysis by gender reveals that girls' performance appears to be more affected than boys' by their parents' divorce/remarriage or the presence of step-family members. Moreover, analysis by heritage reveals that family structure affects the math performance of children of French heritage differently from those of other Canadian heritage, while the impact on reading scores is similar between these two groups. A similar result follows our analysis of religious groups. The impact of family structure differs between children in Catholic families and those in Non-Catholic families for math performance, but is similar for reading performance.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.314
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.129
GPT teacher head0.368
Teacher spread0.240 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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