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Record W2038016106 · doi:10.1300/j074v12n01_08

Unanticipated Consequences: A Comparison of Expected and Actual Retirement Timing Among Older Women

2000· article· en· W2038016106 on OpenAlexaffabout
Barbara Mitchell, Andrew Wister, Gloria Gutman

Bibliographic record

VenueJournal of Women & Aging · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPerspective (graphical)Affect (linguistics)Health and Retirement StudyLogistic regressionLife course approachPsychologySample (material)Demographic economicsGerontologyInequalityRetirement ageSocial psychologyEconomicsMedicineFinancePension

Abstract

fetched live from OpenAlex

The present study adds to the growing body of literature on women and retirement by means of a comparative analysis of the factors associated with anticipated retirement timing (among pre-retirees) and actual retirement timing (among retirees). Adopting a political economy of aging perspective, we argue that socially-structured patterns of gender inequality related to women's multiple roles across the life course affect patterns of retirement timing. Specifically, we hypothesize that the gendered nature of women's work-retirement decision-making is unanticipated during pre-retirement years. Logistic regression analyses are performed on data drawn from a sample of 275 women aged 45 and older living in the Vancouver area of British Columbia. A central finding is that while actual timing of retirement is affected by family caregiving responsibilities and by health/stress factors, pre-retirees do not perceive these to be important in their own expected retirement timing. Implications for social policy, education, and women's financial and psychological well-being in old age are elaborated.

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)0.005
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.177
GPT teacher head0.426
Teacher spread0.249 · 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

Citations63
Published2000
Admission routes2
Has abstractyes

Explore more

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