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Record W2611374779 · doi:10.1037/pag0000173

Greater perceived similarity between self and own-age others in older than young adults.

2017· article· en· W2611374779 on OpenAlexaff
Tian Lin, Elizabeth Ankudowich, Natalie C. Ebner

Bibliographic record

VenuePsychology and Aging · 2017
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsMcGill University
FundersNational Institute on Aging
KeywordsPsychologyPersonalityPsycINFOBig Five personality traitsYoung adultTraitValence (chemistry)Salience (neuroscience)Developmental psychologySocial psychologyMEDLINE

Abstract

fetched live from OpenAlex

As people age, they increasingly incorporate age-stereotypes into their self-view. Based on this evidence we propose that older compared to young adults identify to a greater extent with their own-age group on personality traits, an effect that may be particularly pronounced for positive traits. Two studies tested these hypotheses by examining associations in young and older adults between evaluations of self and own-age others on personality traits that varied on valence. In both studies, young and older participants rated personality trait adjectives on age typicality, valence, and self-typicality. Converging results across both studies showed that older compared to young participants were more likely to endorse personality traits as self-typical when those traits were also perceived as more typical for their own-age group, independent of whether age was made salient to participants prior to evaluation. In addition, there was evidence that the association between evaluations of self and own-age others in older participants was greater for more positive personality traits. This age-differential pattern is discussed in the context of increased age salience in aging and its effect on the similarity between evaluations of self and own-age others in older compared to young adults. (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 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 score0.035
Threshold uncertainty score0.689

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 science0.0000.000
Research integrity0.0000.001
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 head0.052
GPT teacher head0.394
Teacher spread0.342 · 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

Citations16
Published2017
Admission routes1
Has abstractyes

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