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Record W2119375441 · doi:10.1080/03601270903323976

Immunity to Popular Stereotypes of Aging? Seniors and Stereotype Threat

2010· article· en· W2119375441 on OpenAlexaff
Sean Horton, Joseph Baker, William H. Pearce, Janice Deakin

Bibliographic record

VenueEducational Gerontology · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsQueen's UniversityYork UniversityUniversity of Windsor
Fundersnot available
KeywordsStereotype (UML)PsychologyFlexibility (engineering)Stereotype threatRecallIntervention (counseling)CognitionGerontologyQuality of life (healthcare)Social psychologyClinical psychologyDevelopmental psychologyMedicineCognitive psychologyPsychiatry

Abstract

fetched live from OpenAlex

Previous research suggests that seniors' short-term performance is affected by stereotype threat—defined as a situation in which an individual is at risk of confirming a negative characterization about one's group. The current study attempted to replicate and extend these findings to areas of cognitive and physical functioning considered important to seniors' quality of life and known to decline with age. In total, 99 seniors were tested on six dependent measures: recall performance, reaction time, grip strength, flexibility, walking speed, and self-concept. While seniors were affected by the stereotype intervention, they suffered no performance decrements on the main dependent measures. This raises the intriguing possibility that a certain segment of seniors may be immune to popular stereotypes of aging.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.383
Teacher spread0.349 · 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

Citations51
Published2010
Admission routes1
Has abstractyes

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