MétaCan
Menu
Back to cohort
Record W2750170223 · doi:10.1386/jaah.8.2.193_1

Ineffable knowledge: Tensions (and solutions) in art-based research representation and dissemination

2017· article· en· W2750170223 on OpenAlex
Katherine Boydell, Michael Hodgins, Brenda Gladstone, Elaine Stasiulis

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

VenueJournal of Applied Arts and Health · 2017
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDisseminationDilemmaVariety (cybernetics)Representation (politics)The artsAction (physics)SociologyPublic relationsPsychologyVisual artsComputer sciencePolitical scienceEpistemologyArt

Abstract

fetched live from OpenAlex

Abstract This article draws upon an art-based health research (ABHR) study that examines the work of health/social science researchers and artists who use a wide variety of art genres to create and disseminate scientific health-based research. The intent of their work is to reduce the knowledge to action gap as well as to enable engagement with the research findings on the part of the target audience. With respect to the use of art genres to disseminate research findings, the representation of the source material often poses a dilemma for both artists and researchers alike, particularly vis-à-vis the extent to which the research is made explicit. We consider here the methodological and epistemological expectations of the ABHR community (both artists and researchers) regarding dissemination of research findings. We detail the tensions experienced in creative teams engaged in ABHR projects when deciding exactly how much information about the research should be provided to the audience and then move on to highlight the strategies identified by our study participants to address these tensions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.930
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.272
GPT teacher head0.518
Teacher spread0.246 · 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