Conducting creative research: Research ethics and research conduct in art and design
Bibliographic record
Abstract
The application of research ethics and responsible conduct of research standards in art and design scholarship presents unique challenges. Traditional ethics and conduct frameworks, often shaped by biomedical and social science research models, can conflict with creative methodologies. Key issues include tensions between institutional regulations and creative practices, the integration of emergent methods (e.g. research-creation), the complexity of overlapping professional roles, and the unique ethical obligations of working in Indigenous research contexts. Drawing on Canadian case studies from Emily Carr University of Art + Design and OCAD University, this topics piece highlights three collaborative initiatives—(1) a series of workshops and presentations, (2) the development of open access educational resources, and (3) the launch of a student research design award—all of which strive to bridge gaps in understanding and compliance. These efforts emphasize the need for flexible, collaborative, and context-sensitive approaches. Ongoing critical engagement and adaptation in research ethics and conduct education, particularly in regards to emerging technologies and the growth of interdisciplinary research practices, is vital.
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 | 0.348 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.018 | 0.171 |
| Scholarly communication | 0.053 | 0.027 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.013 | 0.022 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".