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Record W4406148607 · doi:10.1177/01650254241310168

Giving back to the future: Generativity, future time perspective, and purpose

2025· article· en· W4406148607 on OpenAlexaff
K. Hill, Nathan A. Lewis, Laura Dewitte, Mathias Allemand, Patrick L. Hill

Bibliographic record

VenueInternational Journal of Behavioral Development · 2025
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGenerativityPsychologyModerationPath analysis (statistics)Perspective (graphical)Association (psychology)Developmental psychologyFile Transfer ProtocolTime perspectiveSocial psychology

Abstract

fetched live from OpenAlex

Research suggests that generativity may serve as a path to the experience of purpose in life. However, little is known about how this relationship is influenced by age and future time perspective (FTP). The present study aimed to investigate age and FTP as moderators of the association between generativity and sense of purpose. A total of 787 participants ranging in age from 19 to 94 years ( M = 50.23; SD = 16.47) completed self-report measures of generativity, FTP, and sense of purpose as part of a broader study. Correlational analyses revealed that generativity, age, and FTP were positively associated with a sense of purpose. Moderation analyses found that FTP, but not age, significantly moderated the relationship between generativity and sense of purpose. Specifically, individuals with a more limited FTP exhibited a stronger association between generativity and purpose. Findings lend support for a robust association between generativity and sense of purpose ( r = .54), and they suggest that generativity may be particularly important for sense of purpose when time is perceived as limited. This study paves the way for future research examining the role of FTP on the development of generativity across the lifespan.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.000
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 head0.033
GPT teacher head0.404
Teacher spread0.371 · 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.

Study designOther design
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

Citations3
Published2025
Admission routes1
Has abstractyes

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