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Record W2730301458 · doi:10.1093/geroni/igx004.4740

I DO LIKE THE WAY I LOOK: OLDER MEN’S PERCEPTIONS AND EXPERIENCES OF AGING AND BODY IMAGE

2017· article· en· W2730301458 on OpenAlexaffabout
Laura Hurd Clarke, Raveena Mahal

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldPsychology
TopicAging and Gerontology Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerceptionImage (mathematics)PsychologyGerontologyMedicineComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

This paper examines the body images of older Canadian men, focusing on their perceptions of and feelings about their changing, aging bodies. We draw upon data from in-depth interviews with 22 community-dwelling men, aged 65 to 89, who were diverse with respect to marital status, level of education, employment history, and household income. Our thematic analysis revealed three key ways that the men perceived and assessed their bodies. First, our participants were either pleased with their overall appearances or unconcerned about how their looks had altered over time. Second, the men emphasized the importance of bodily function over aesthetics as they highlighted their functional qualities (e.g. strength, independence, etc.) and physical activities (e.g. leisure pursuits). Finally, the men articulated concerns about future losses to their health and how such changes might undermine their independence. We consider our findings in relation to the socio-cultural theorizing about gender, health, and body image.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.570
Threshold uncertainty score0.864

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.005
Scholarly communication0.0030.001
Open science0.0000.002
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 head0.046
GPT teacher head0.405
Teacher spread0.359 · 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 designQualitative
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

Citations1
Published2017
Admission routes2
Has abstractyes

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