Ineffable knowledge: Tensions (and solutions) in art-based research representation and dissemination
Bibliographic record
Abstract
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.
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 | 0.263 | 0.373 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.021 | 0.097 |
| Scholarly communication | 0.045 | 0.034 |
| Open science | 0.008 | 0.038 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".