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Record W2828500297 · doi:10.1093/geront/gnw162.728

RETIREMENT SECURITY AMONG THE NEVER MARRIED POPULATION

2016· article· en· W2828500297 on OpenAlexaff
Erin Relyea, Raza Mirza, Sorcha MacLeod, Shirley Musich, Kevin Hawkins, David Armstrong

Bibliographic record

VenueThe Gerontologist · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsUniversity of TorontoInstitute for Work & Health
Fundersnot available
KeywordsPopulationPsychologyDemographic economicsDemographyGerontologySociologyMedicineEconomics

Abstract

fetched live from OpenAlex

older adults' psychological well-being, physical and cognitive functioning, and survival (Ory et al., 2003). In correlation with the pressures of societal expectations, beauty companies have implicated marketing strategies that appeal to the public through ageist campaigns, both overt and covert, that seek to gain profit through the exploitation of aging processes. By monitoring overt and covert displays of ageism in the marketing appeals of health and beauty television commercials, this study aims to explore any major shifts in ageist marketing appeals. By comparing three of the most highly marketed competitors in the cosmetic industry: L'Oral, Estee Lauder, and Revlon, changes in ageist marketing techniques will be analyzed. Based on a preliminary evaluation of the ageist marketing appeals of the L'Oral Paris cosmetic company, it is hypothesized that, upon further analysis, the three afore mentioned health and beauty companies will show a majorly positive correlation between the prevalence of overt and covert ageist marketing appeals in health and beauty commercials and the year of airing, from 1980 to 2015.

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 score0.492
Threshold uncertainty score0.973

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 studies0.0010.001
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 head0.026
GPT teacher head0.297
Teacher spread0.272 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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