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Record W3112118797 · doi:10.1017/s1474746420000627

The Management of Retirement Savings Among Financially Heterogamous Couples

2020· article· en· W3112118797 on OpenAlexaffabout
Maude Pugliese, Hélène Belleau

Bibliographic record

VenueSocial Policy and Society · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsSpouseEconomicsDemographic economicsVulnerability (computing)Retirement ageDuration (music)Population ageingHealth and Retirement StudySavings accountBalance (ability)PopulationLabour economicsFinancePensionSociologyDemographyPsychology

Abstract

fetched live from OpenAlex

Retirement scholars (and policy makers) have traditionally assumed spouses share their retirement savings, even when they are financially heterogamous and their individual saving capacities diverge. Recent research, however, has challenged this assumption, emphasising that wealth is unequally distributed within couples. In this study, we contribute to this debate by exploring how often financially heterogamous spouses describe their management of retirement savings as joint and redistributive. Data collected in Québec (Canada) in 2015 show that 28 per cent of couples with an income differential report to balance retirement savings across partners. Building on exchange and institutional theories of conjugal behaviour, we also stress that the prevalence of this practice varies with several factors, including union duration and matrimonial status. These findings suggest policy makers underestimate the size of the population at risk of old age financial vulnerability when assuming lower-income individuals are well prepared for retirement if partnered with a better-off spouse who saves.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.023
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.018
GPT teacher head0.288
Teacher spread0.270 · 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

Citations13
Published2020
Admission routes2
Has abstractyes

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