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Record W2332016013 · doi:10.1037/pag0000082

Age differences in personal values: Universal or cultural specific?

2016· article· en· W2332016013 on OpenAlexaffabout
Helene H. Fung, Yuen Wan Ho, Rui Zhang, Xin Zhang, Kimberly A. Noels, Kim‐Pong Tam

Bibliographic record

VenuePsychology and Aging · 2016
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyCollectivismPsycINFOCultural valuesSocial psychologyIndividualismSocial value orientationsDevelopmental psychologyCross-cultural studiesWell-beingMEDLINEGender studiesSociology

Abstract

fetched live from OpenAlex

Prior studies on value development across adulthood have generally shown that as people age, they espouse communal values more strongly and agentic values less strongly. Two studies investigated whether these age differences in personal values might differ according to cultural values. Study 1 examined whether these age differences in personal values, and their associations with subjective well-being, showed the same pattern across countries that differed in individualism-collectivism. Study 2 compared age differences in personal values in the Canadian culture that emphasized agentic values more and the Chinese culture that emphasized communal values more. Personal and cultural values of each individual were directly measured, and their congruence were calculated and compared across age and cultures. Findings revealed that across cultures, older people had lower endorsement of agentic personal values and higher endorsement of communal personal values than did younger people. These age differences, and their associations with subjective well-being, were generally not influenced by cultural values. (PsycINFO Database Record

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.141
GPT teacher head0.385
Teacher spread0.244 · 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 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

Citations77
Published2016
Admission routes2
Has abstractyes

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