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Record W2108781495 · doi:10.1177/0733464803253588

Seniors Say the `Darndest' Things about Exercise: Quotable Quotes that Stimulate Applied Gerontology

2003· article· en· W2108781495 on OpenAlexaff
Sandy O’Brien Cousins

Bibliographic record

VenueJournal of Applied Gerontology · 2003
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMeaning (existential)NarrativePerspective (graphical)GerontologyPsychologyPhysical activityQualitative researchSociologyMedicinePsychotherapistSocial scienceArt

Abstract

fetched live from OpenAlex

This review article highlights unusual narrative elements spoken by older people as they present their perspective on and involvement in exercise, sports, and physical activity. Drawn from a number of studies, the voices of elders are assembled here as quotable quotes or statements that challenge contemporary understanding and meaning. Just as elderly respondents are wondering what kind of research is going on in applied gerontology, researchers are perplexed when some elders say `the darndest things' about their health, lifestyles, and physical activity. Overall, the review serves as a collection of learnings that may help other applied gerontologists by (a) sharing some insights and challenges of doing qualitative research with elders, (b) exploring how older people make sense of the role of physical activity in their lives, and (c) showing how interpretive approaches can enrich current understanding about the lifestyles and life meaning of 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 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)0.012
metaresearch head score (Gemma)0.024
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 score0.012
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0030.005
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.314
Teacher spread0.268 · 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

Citations7
Published2003
Admission routes1
Has abstractyes

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