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Record W4229456564 · doi:10.1177/19408447221097063

Interweaving Arts-Based, Qualitative and Mixed Methods Research: Showcasing Integration and Knowledge Translation Through Material and Narrative Reflection

2022· article· en· W4229456564 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Review of Qualitative Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsThe artsMateriality (auditing)NarrativeExhibitionSociologyPerspective (graphical)Reflection (computer programming)Qualitative researchNarrative inquiryVisual artsEpistemologyComputer scienceAestheticsSocial scienceArt

Abstract

fetched live from OpenAlex

Arts-based research can exist as a stand-alone method, methodology, or reflect varying degrees of interweaving with other research approaches. With this in mind, this paper explores these relationships using examples from a recent arts-based research exhibition inclusive of various artistic works created to respond to, understand, and reflect nuanced experiences, narratives, contradictions, and diverse data sources in frailty and aging research. Taking an interdisciplinary perspective positioned between the arts and health sciences with specific attention to knowledge translation, the roles of object materiality, proximity to research data, and narrative reflection are considered, as are their implications for the creation and purpose of arts-based research more generally. The paper encourages researchers to consider how research data and arts-based research can continue to evolve and create deeply impactful and resonating findings.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.150
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1500.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.959
GPT teacher head0.844
Teacher spread0.115 · 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