MétaCan
Menu
Back to cohort
Record W4389196009 · doi:10.1177/02654075231214005

Young adults’ experiences of ageism in the United Kingdom: Forms, sources, and associations with intergenerational attitudes

2023· article· en· W4389196009 on OpenAlexaboutno aff
Craig Fowler, Jessica Gasiorek

Bibliographic record

VenueJournal of Social and Personal Relationships · 2023
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)PsychologyYoung adultMiddle ageOlder peopleAge discriminationGerontologyDemographyDevelopmental psychologyMedicineSociology

Abstract

fetched live from OpenAlex

Although previous research suggests that a large proportion of young adults experience ageism, information is scarce regarding exactly how often they encounter different forms of age-based discrimination. To address this lacuna, we recruited young adults from the U.K. to complete four weekly surveys in which they reported the number of days during the preceding week on which they experienced various forms of ageism. More than three-quarters of our respondents experienced some form of ageism at least once during the reporting period, and more than one-quarter of respondents experienced ageism (on average) at least once per week during the reporting period. The most oft-encountered forms of ageism encountered by young adults involved being shown a lack of respect/being patronized and having other people make assumptions about their cognitive or social characteristics. Most commonly, the perpetrators of ageism were middle-aged and later middle-aged persons (rather than older people) encountered in the course of employment. The number of days on which young adults experienced ageism was inversely correlated with the degree to which they believed middle-aged and later-middle aged adults held positive stereotypes of young adults, and positively predicted the desire to avoid interaction with middle-aged, late middle-aged, and older adults.

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 score0.212
Threshold uncertainty score0.278

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.162
GPT teacher head0.390
Teacher spread0.228 · 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

Citations8
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Social and Personal RelationshipsSame topicAging and Gerontology ResearchFrench-language works237,207