Audience engagement and impact: Ethical considerations in art-based health research
Bibliographic record
Abstract
Abstract Art-based research presents epistemological benefits and challenges for researchers and artists. There are also significant ethical implications for audiences as well as participants and researchers. We argue that it is important to consider who is in the audience and how to minimize potentially harmful effects of the work. This includes issues of privacy and confidentiality arising from incorporation of participants’ stories into the art form and the need to offer reassurance to audience members who may believe they recognize aspects of themselves or someone they know in the production. It also highlights the need for researchers to establish respectful terms of engagement for audiences who may offer a variety of interpretations to the artistic work. We point to possible ways that researchers and artists can address these ethical tensions to ensure that art-based health research is ethically rigorous as well as being creatively engaging.
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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 | 0.456 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.024 | 0.111 |
| Scholarly communication | 0.033 | 0.020 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.018 | 0.018 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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".