The Demography of Multigenerational Caregiving: A Critical Aspect of the Gendered Life Course
Why this work is in the frame
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Bibliographic record
Abstract
Multigenerational caregiving is important because it affects social and economic outcomes. Existing studies usually exclude theoretically and empirically important aspects—emotional care and horizontal care—that may systematically underestimate gender differences. In this study, we comprehensively describe caregiving by gender and age and examine how sensitive estimates are to the inclusion of directions and types of care. Using the Generations and Gender Survey (GGS) in Europe (N = 114,147), we find that women are more likely to provide care than men across the life course, and gender gaps are largest during critical periods for human capital accumulation. Significant gender gaps in favor of more women providing care are found in most countries, especially when emotional caregiving is included, but in some countries, more men provide care at the oldest ages. These findings highlight how measuring caregiving well is critical to understanding the gendered life course.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it