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Record W4385567671 · doi:10.1162/leon_a_02372

<i>Piece of Mind</i>: Mobilizing Scientific and Experiential Knowledge of Dementia through the Arts

2023· article· en· W4385567671 on OpenAlexaff
Naila Kuhlmann, Jennifer Lécuyer, Aliki Thomas, Stefanie Blain‐Moraes

Bibliographic record

VenueLeonardo · 2023
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsCentre for Interdisciplinary Research in RehabilitationWeyerhauser (Canada)McGill University
Fundersnot available
KeywordsThe artsExperiential knowledgeEmpathyExperiential learningDementiaPsychologyDisseminationPerforming artsSociology of scientific knowledgeEngineering ethicsSociologyKnowledge managementComputer scienceDiseaseEpistemologyPedagogyPolitical scienceSocial psychologySocial scienceMedicineVisual artsEngineering

Abstract

fetched live from OpenAlex

Abstract While peer-reviewed articles and conferences are appropriate for disseminating research findings within academia, they are less effective for translating scientific knowledge into meaningful and practical applications. Moreover, exchanging knowledge with nonacademic stakeholders is a crucial yet often overlooked step in ensuring that research aligns with the needs and reality of knowledge users. This is particularly problematic in dementia and Alzheimer’s disease research, where social stigma and the reliance on quantitative and self-report methods hamper meaningful dialogue between academic researchers, nonacademic stakeholders, and the broader community. The authors’ project Piece of Mind uses performing arts to create common ground for knowledge exchange, facilitate empathy through creative collaboration, and improve public awareness of dementia.

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)0.005
metaresearch head score (Gemma)0.009
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 score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.008
Scholarly communication0.0050.003
Open science0.0010.010
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.040
GPT teacher head0.341
Teacher spread0.302 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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