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Record W2983741036 · doi:10.1093/geroni/igz038.1405

THE SOCIAL CONSTRUCTION OF RETIREMENT AND GENDER DIFFERENCES IN RETIREMENT TIMING

2019· article· en· W2983741036 on OpenAlexaffabout
Michelle Pannor Silver

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicRetirement, Disability, and Employment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPensionIntergenerational equityEquity (law)Retirement ageConstruct (python library)Government (linguistics)Work (physics)Labour economicsCompensation (psychology)Social equalitySocial securityEconomicsPolitical scienceDemographic economicsPsychologySocial psychologyFinanceLaw

Abstract

fetched live from OpenAlex

Abstract Retirement is an ever-evolving, dynamic, and complex social construct we associate with the end of one’s career. For some the term is a bad word and a term that needs to be retired, while others can’t wait to retire and enjoy the good life. This paper examines a brief history of retirement and theoretical work from feminist gerontology, while focusing on gender differences in the social construction of retirement and policy implications of 10 different government pension plans. In doing so, it looks at policy implications associated with the standard retirement age tied to public pension plans in the United States, Canada, and the European Union. Findings indicate that women live longer than men in each country, yet women retire earlier and receive lower pensions than men. As the landscape surrounding women’s work experiences changes and concerns about gender equity in salaries and workplace compensation continue to be raised, this paper extends the concerns to raise important questions about inequities in retirement.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.101
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.259
GPT teacher head0.426
Teacher spread0.166 · 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 teacher head, 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

Citations1
Published2019
Admission routes2
Has abstractyes

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