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Record W4406857431 · doi:10.1080/01490400.2025.2454524

From “the Tragic Self” to “the Empowered Self”: How Leisure Spaces Can Foster Narrative Agency for People with Lived Experience of Dementia

2025· article· en· W4406857431 on OpenAlexaff
Colleen Reid, Ania Landy, Julia Henderson

Bibliographic record

VenueLeisure Sciences · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsUniversity of British ColumbiaDouglas College
Fundersnot available
KeywordsNarrativeAgency (philosophy)Lived experienceDementiaSociologySelfPsychologyAestheticsNarrative inquiryGender studiesSocial psychologyPsychoanalysisMedicineArtSocial scienceLiterature

Abstract

fetched live from OpenAlex

Harmful cultural narratives equate dementia with a loss of self, citizenship, narrative agency. Although leisure activities are widespread in dementia programs, less attention is paid to leisure’s social justice orientation and potential to resist dominant narratives. Raising the Curtain on the Lived Experiences of Dementia was a five-year community-based participatory research study, guided by the values and practices of social citizenship (Bartlett & O’Connor, Citation2007, Citation2010). The project explored how individuals living with dementia made sense of their lived experiences individually and collectively. Analysis of participants’ accounts of their dementia revealed the tragic self and the empowered self as two over-arching themes, showing how participants’ both adopted and resisted dominant dementia narratives. Insights suggest leisure spaces have potential to foster the narrative agency of individuals living with dementia to enable expression of the fullness of their experiences including powerlessness and agency; uncertainty and certainty; dread and joy; conformity and resistance.

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)0.009
metaresearch head score (Gemma)0.011
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 score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0140.032
Scholarly communication0.0140.012
Open science0.0020.024
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.303
Teacher spread0.280 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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