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Record W2299019041 · doi:10.1111/jomf.12286

The Changing Demography of Grandparenthood

2016· article· en· W2299019041 on OpenAlexaffabout
Rachel Margolis

Bibliographic record

VenueJournal of Marriage and the Family · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsWestern University
Fundersnot available
KeywordsGrandparentChildlessnessPostponementFertilityDemographyAffect (linguistics)PopulationTotal fertility ratePsychologyGerontologyDevelopmental psychologyResearch methodologyMedicineSociologyFamily planningEconomics

Abstract

fetched live from OpenAlex

Demographic changes affect the time that individuals spend in different family roles. Mortality decline increases the time an individual can spend as a grandparent, but childlessness decreases the proportion of people who ever become grandparents, and fertility postponement delays when grandparenthood begins. This article examines changes in the length of grandparenthood at the population level and why it has changed in Canada over a 26‐year period. Using the Sullivan method, years spent as a grandparent are estimated by sex for 1985 and 2011. Results show that grandparenthood is coming significantly later to Canadians, in small part due to increased childlessness and in large part to fertility postponement of respondents and their children. The average length of grandparenthood decreased among women from 24.7 to 24.3 years but increased among men from 17.0 to 18.9 years. The changing timing and length of grandparenthood have implications for multigenerational relationships and intergenerational transfers.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.796
Threshold uncertainty score0.410

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.007
GPT teacher head0.242
Teacher spread0.236 · 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

Citations94
Published2016
Admission routes2
Has abstractyes

Explore more

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