Three-generation family households in early childhood: Comparisons between the United States, the United Kingdom, and Australia
Bibliographic record
Abstract
BACKGROUND: Shifting demographic trends in the United States (US) have resulted in increasing numbers of three-generation family households, where a child lives with a parent(s) and grandparent(s). Although similar demographic trends have been occurring in the United Kingdom (UK) and Australia, very little research has studied three-generation coresidence in these countries and no research has documented trends cross-nationally. OBJECTIVE: We investigate differences in the rates of three-generation coresidence in early childhood cross-nationally. METHOD: This study uses three longitudinal birth cohort studies: the Early Childhood Longitudinal Study - Birth Cohort for the US, the Millennium Cohort Study for the UK, and the Longitudinal Study of Australian Children - Birth Cohort to investigate cross-national differences three-generation coresidence in early childhood. RESULTS: We find that nearly one quarter of US children live in a three-generation household during early childhood, compared with 8% of children in the UK and 11% in Australia. Although there are large differences in the frequency of coresidence cross-nationally, we find that similar demographic groups live in three-generation households across contexts. In general, younger, less educated, lower income and minority mothers are more likely to live in three-generation households in all three countries.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".