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Record W2053586823 · doi:10.1521/soco.2006.24.3.218

Looking to the Future: How Possible Aged Selves Influence Prejudice Toward Older Adults

2006· article· en· W2053586823 on OpenAlexaff
Dominic J. Packer, Alison L. Chasteen

Bibliographic record

VenueSocial Cognition · 2006
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologySalience (neuroscience)Prejudice (legal term)Perspective (graphical)Young adultDevelopmental psychologyAffect (linguistics)Social identity theoryIdentity (music)Social psychologyExpression (computer science)Social groupCognitive psychology

Abstract

fetched live from OpenAlex

Ageism is thought to be a unique form of prejudice due to the fact that humans change age group memberships as they age and grow old. The current research investigated how increasing the salience of their future aged selves would affect young adults' expressions of prejudice toward older adults. Taking a social identity approach, we hypothesized that young adults' identification with their current age group would moderate the effects of a future self exercise on ageism. In two studies, strongly identified young adults expressed less positive attitudes toward older adults after writing about themselves at 70, perhaps because thoughts of aged selves were threatening to them. Conversely, the future self exercise increased positive attitudes among weakly identified young adults in Study 2. Study 2 also demonstrated the effects of imagining a future self to be distinct from the effects of a perspective–taking exercise. The role of future social identity concerns in the expression of ageism is discussed.

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)0.003
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.324
Teacher spread0.305 · 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

Citations46
Published2006
Admission routes1
Has abstractyes

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