MétaCan
Menu
Back to cohort

An arts-based knowledge translation (ABKT) planning framework for researchers

2017· article· en· W2737680143 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

VenueEvidence & Policy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsQueen's University
Fundersnot available
KeywordsThe artsProcess (computing)Computer scienceKnowledge translationFoundation (evidence)Knowledge managementSociologyMultimediaVisual artsPolitical scienceArt

Abstract

fetched live from OpenAlex

Arts-based knowledge translation (ABKT) is a process that uses diverse art genres (visual arts, performing arts, creative writing, multimedia including video and photography) to communicate research with the goal of catalysing dialogue, awareness, engagement, and advocacy to provide a foundation for social change on important societal issues. We propose a four-stage ABKT planning framework for researchers: (1) setting goals of ABKT by target audiences; (2) choosing art form, medium, dissemination strategies, and methods for collecting impact data; (3) building partnerships for co-production; and (4) assessing impact. The framework is derived from examples across sectors of the different art forms currently being used in ABKT, and discusses how researchers have attempted to evaluate the impact of their ABKT efforts. Ultimately, our goal is to provide a practical ABKT framework to assist researchers, but more work is needed to explore the four dimensions in practice.

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)0.013
metaresearch head score (Gemma)0.080
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.080
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.959
GPT teacher head0.809
Teacher spread0.150 · 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