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Record W2127278729 · doi:10.1093/geronb/57.3.p256

Age Excuses: Conversational Management of Memory Failures in Older Adults

2002· article· en· W2127278729 on OpenAlexafffund
Ellen B. Ryan, Sherrie Bieman-Copland, Sheree T. Kwong See, Constance Ellis, Ann P. Anas

Bibliographic record

VenueThe Journals of Gerontology Series B · 2002
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsQueen's UniversityUniversity of AlbertaBrock UniversityMcMaster University
FundersSocial Sciences and Humanities Research Council of CanadaMcMaster University
KeywordsExcuseWorryPsychologyForgettingDevelopmental psychologySocial psychologyCognitive psychologyAnxietyPsychiatry

Abstract

fetched live from OpenAlex

The social consequences of appealing to age to excuse memory failure were examined in 2 vignette-based studies. In Study 1, 75 older (M = 72 years) and 78 young (M = 22 years) adults evaluated forgetful older targets in their 70s who used their age, lack of ability, lack of effort, or the situation to explain forgetting. In Study 2, 105 older (M = 72 years) and 105 young participants (M = 19 years) evaluated forgetful targets with no specific age given in 4 excuse conditions (age, ability, situation, and no excuse). In support of the prediction of positive consequences, age excuses were rated as more believable than situation in both studies and more believable and socially fluent than effort in Study 1. In support of predictions of negative consequences, both groups in Study 2 rated target persons who used an age excuse to be much older than their peers and, along with ability excuse users, as eliciting more worry and frustration than the others. Moreover, young adults showed additional sensitivity to the negative aspects of age excuses in terms of worry and frustration in Study 1 and anticipated repeat forgetting in Study 2. These results suggest that although age excuses may relieve socially awkward situations, this strategy reinforces negative age stereotyping of the older forgetter.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.994

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)0.0070.000

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 head0.071
GPT teacher head0.356
Teacher spread0.285 · 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.

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

Citations32
Published2002
Admission routes2
Has abstractyes

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