An arts-based knowledge translation (ABKT) planning framework for researchers
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.143 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".