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Record W2146238370 · doi:10.1093/geront/gns001

Time for My Life Now: Early Boomer Women's Anticipation of Volunteering in Retirement

2012· article· en· W2146238370 on OpenAlexaffabout
Patricia Seaman

Bibliographic record

VenueThe Gerontologist · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBaby boomersAnticipation (artificial intelligence)Volunteer workScheduleService (business)DemographicsPsychologyWork (physics)Public relationsWork scheduleHindsight biasSocial psychologySociologyManagementPolitical scienceMarketingBusinessDemographic economicsEconomics

Abstract

fetched live from OpenAlex

PURPOSE: This study explored to what extent early Boomer women who work for pay will be interested in and committed to formal volunteering during retirement. METHOD: Data for this hermeneutic study were gathered through 2 in-depth conversational interviews of 19 English-speaking early Boomer women living in New Brunswick, Canada. RESULTS: Interpretive analysis of interview data revealed that for these early Boomer women, consideration of volunteering in retirement revolves around analyzing the perceived costs and benefits, setting specific criteria for involvement, and recognizing the societal impacts of their refusal to volunteer or their limitation of commitment. IMPLICATIONS: Although not generalizable, the results of this study suggest administrators planning to recruit and retain retired early Boomer women to volunteer should not assume participation at the same rate or with the same commitment as previous generations. New models of volunteer recruitment and deployment may need to be developed to meet the expectations of these women. These participants indicated that formal volunteering will be for personal, not altruistic reasons, on their own terms through direct service; they are not interested in the consuming commitments of board and committee work or fundraising. Volunteering must be meaningful, something about which they are passionate and on their own schedule.

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)0.001
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 score0.172
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

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

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 head0.056
GPT teacher head0.337
Teacher spread0.281 · 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

Citations25
Published2012
Admission routes2
Has abstractyes

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