Ethics in Community-University-Artist Partnered Research: Tensions, Contradictions and Gaps Identified in an ‘Arts for Social Change’ Project
Bibliographic record
Abstract
Academics from diverse disciplines are recognizing not only the procedural ethical issues involved in research, but also the complexity of everyday “micro” ethical issues that arise. While ethical guidelines are being developed for research in aboriginal populations and low-and-middle-income countries, multi-partnered research initiatives examining arts-based interventions to promote social change pose a unique set of ethical dilemmas not yet fully explored. Our research team, comprising health, education, and social scientists, critical theorists, artists and community-activists launched a five-year research partnership on arts-for-social change. Funded by the Social Science and Humanities Research Council in Canada and based in six universities, including over 40 community-based collaborators, and informed by five main field projects (circus with street youth, theatre by people with disabilities, dance for people with Parkinson’s disease, participatory theatre with refugees and artsinfused dialogue), we set out to synthesize existing knowledge and lessons we learned. We summarized these learnings into 12 key points for reflection, grouped into three categories: community-university partnership concerns ( n = 3), dilemmas related to the arts ( n = 5), and team issues ( n = 4). In addition to addressing previous concerns outlined in the literature (e.g., related to consent, anonymity, dangerous emotional terrain, etc.), we identified power dynamics (visible and hidden) hindering meaningful participation of community partners and university-based teams that need to be addressed within a reflective critical framework of ethical practice. We present how our team has been addressing these issues, as examples of how such concerns could be approached in community-university partnerships in arts for social change.
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 | 0.279 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.051 | 0.098 |
| Scholarly communication | 0.037 | 0.026 |
| Open science | 0.007 | 0.045 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".