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Record W2963733800 · doi:10.1177/2378023119862737

The Demography of Multigenerational Caregiving: A Critical Aspect of the Gendered Life Course

2019· article· en· W2963733800 on OpenAlexafffund
Sarah Patterson, Rachel Margolis

Bibliographic record

VenueSocius Sociological Research for a Dynamic World · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health ResearchPennsylvania State University
KeywordsLife course approachGender gapInclusion (mineral)PsychologyHuman capitalDemographyGerontologyDevelopmental psychologyDemographic economicsSociologyMedicineSocial psychologyEconomic growthEconomics

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.077
GPT teacher head0.433
Teacher spread0.356 · 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

Citations65
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueSocius Sociological Research for a Dynamic WorldSame topicIntergenerational Family Dynamics and CaregivingFrench-language works237,207