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Record W4387949713 · doi:10.1037/pag0000782

Age differences in the experience of everyday happiness: The role of thinking about the future.

2023· article· en· W4387949713 on OpenAlexafffundabout
Yoonseok Choi, Jennifer C. Lay, Minjie Lu, Da Jiang, Matthew Peng, Helene H. Fung, Peter Graf, Christiane A. Hoppmann

Bibliographic record

VenuePsychology and Aging · 2023
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of British Columbia
FundersChinese University of Hong KongUniversity of British ColumbiaCanada Research ChairsVancouver Foundation
KeywordsHappinessPsychologyModerationPsycINFOAffect (linguistics)Positive psychologyContext (archaeology)ArousalDevelopmental psychologyEveryday lifeAssociation (psychology)Social psychologyMEDLINE

Abstract

fetched live from OpenAlex

= 24.6; 68% female; 77% Asian [East Asian, South Asian, and Southeast Asian]; 73% postsecondary educated), combining four data sets collected at two locations (Vancouver, Canada; Hong Kong) with different age samples (older and younger adults). Participants provided up to 30 repeated daily life assessments of momentary affective states and thoughts about the future, over 10 days. Results replicate previous findings by showing that happiness was more strongly associated with low-arousal positive affect and more weakly associated with high-arousal positive affect among older compared to younger adults. Engagement in thinking about the future was higher among younger compared to older adults in general, but its role in moderating the association between happiness and positive affect varying in arousal levels was confounded by the age moderation. Separate analyses conducted for each age group indicate different roles of everyday thinking about the future in shaping happiness experiences for different age groups. Age and future thinking-related contours of happiness are discussed in the context of emotional aging theories. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

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 score0.328
Threshold uncertainty score0.302

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.051
GPT teacher head0.389
Teacher spread0.338 · 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

Citations2
Published2023
Admission routes3
Has abstractyes

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