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Record W2913381375 · doi:10.1111/cars.12228

Wanting To Be Remembered: Intrinsically Rewarding Work and Generativity in Early Midlife

2019· article· en· W2913381375 on OpenAlexaffabout
Jiawen Chen, Harvey Krahn, Nancy L. Galambos, Matthew D. Johnson

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2019
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGenerativityPsychologyFeelingDevelopmental psychologySocial psychologyCivic engagement

Abstract

fetched live from OpenAlex

Previous research on generativity, the desire to leave a legacy through establishing and guiding the next generation, has focused primarily on family life and civic engagement as pathways to midlife generativity. This paper proposes that intrinsically rewarding work can also be associated with a heightened sense of generativity in midlife. We test this hypothesis with data (n = 369 employed individuals, approximately 43 years old) from the 2010 wave of the Edmonton Transitions Study. Civic engagement was positively associated with midlife generativity, as predicted, but the hypothesized positive relationship between generativity and perceived parenting success was not found. Taking into account civic engagement and perceived parenting success, and controlling on a range of other variables, intrinsically rewarding work was positively associated with midlife feelings of generativity.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.054
GPT teacher head0.303
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 designQualitative
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

Citations7
Published2019
Admission routes2
Has abstractyes

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