MétaCan
Menu
Back to cohort
Record W4307360828 · doi:10.4324/9781003260691

Dance, Ageing and Collaborative Arts-Based Research

2022· book· en· W4307360828 on OpenAlexafffund
Rachel Herron, Rachel J. Bar, Mark W. Skinner

Bibliographic record

Venuenot available
Typebook
Languageen
FieldPsychology
TopicDiversity and Impact of Dance
Canadian institutionsToronto Metropolitan UniversityTrent UniversityBrandon University
FundersCanadian Institutes of Health ResearchStanford Bio-XCanada Research ChairsPublic Health AgencyUniversity of BristolUniversity of OxfordTrent UniversityYork UniversityAlzheimer SocietyKoneen SäätiöUniversity of TorontoStrongPublic Health Agency of Canada
KeywordsDanceThe artsVisual artsSociologyArt

Abstract

fetched live from OpenAlex

Dance, Ageing and Collaborative Arts-Based Research contributes a critical and comprehensive perspective on the role of the arts –specifically dance – in enhancing the lives of older people. The book focuses on the development of an innovative arts-based program for older adults and the collaborative process of exploring and understanding its impact in relation to ageing, social inclusion, and care. It offers a wide audience of readers a richer understanding of the role of the arts in ageing and life enrichment, critical contributions to theories of ageing and care, specific approaches to arts-based collaborative research, and an exploration of the impact of Sharing Dance from the perspective of older adults, artists, researchers, and community leaders. Given the interdisciplinary and collaborative nature of this book, it will be of interest across health, social science, and humanities disciplines, including gerontology, sociology, psychology, geography, nursing, social work, and performing arts. Licence line: Creative Commons Attribution-Non Commercial-No Derivatives 4.0 license.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.007
Scholarly communication0.0060.004
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

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 head0.182
GPT teacher head0.434
Teacher spread0.252 · 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
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicDiversity and Impact of DanceFrench-language works237,207