MétaCan
Menu
Back to cohort
Record W2010473277 · doi:10.1177/1077800411427852

Employing the Arts in Research as an Analytical Tool and Dissemination Method

2011· article· en· W2010473277 on OpenAlexaff
Jennifer Lapum, Perin Ruttonsha, Kathryn Church, Terrence M. Yau, Alison Matthews David

Bibliographic record

VenueQualitative Inquiry · 2011
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsToronto General HospitalUniversity of TorontoToronto Metropolitan University
FundersSigma Theta Tau International
KeywordsNarrativeExhibitionSurrenderCuriosityThe artsPoetryVisual artsPhotographySociologyAutoethnographyAestheticsProcess (computing)PsychologyArtComputer scienceHistoryLiteratureSocial psychologySocial science

Abstract

fetched live from OpenAlex

The process to knowing entails perpetual curiosity as well as wearied surrender in which one’s understandings transform. This philosophy describes the approach that our team took to research, interpret and exhibit patients’ narratives of open-heart surgery in “The 7,024th Patient” project – an arts-informed, narrative study that resulted in an installation that is 1,739 square feet in area and over 9 feet in height. With the intention to physically and emotionally engage viewers, patients’ stories were aesthetically translated into an installation of poetry and photography that was configured as a winding, labryinth-like path. In this article, we recount the journey of creating “The 7,024th Patient” exhibition illustrating the employment of the arts as a tool in research for acquiring understanding. In order to vividly highlight our journey, poetic excerpts and photographic images from the installation are embedded.

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)0.230
metaresearch head score (Gemma)0.165
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.770
Threshold uncertainty score0.950

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2300.165
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0120.010
Science and technology studies0.0070.022
Scholarly communication0.0120.007
Open science0.0040.012
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0130.002

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 head0.586
GPT teacher head0.646
Teacher spread0.060 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations72
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueQualitative InquirySame topicEmpathy and Medical EducationFrench-language works237,207