Arts-based health research and academic legitimacy: transcending hegemonic conventions
Bibliographic record
Abstract
Using the Canadian context as a case study, the research reported here focuses on in-depth qualitative interviews with 36 researchers, artists and trainees engaged in ‘doing’ arts-based health research (ABHR). We begin to address the gap in ABHR knowledge by engaging in a critical inquiry regarding the issues, challenges and benefits of ABHR methodologies. Specifically, this paper focuses on the tensions experienced regarding academic legitimacy and the use of the arts in producing and disseminating research. Four central areas of tension associated with academic legitimacy are described: balancing structure versus openness and flexibility; academic obligations of truth and accuracy; resisting typical notions of what counts in academia; and expectations vis-à-vis measuring the impact of ABHR. We argue for the need to reconsider what counts as knowledge and to reconceptualize notions of evaluation and rigor in order to effectively support the effective production and dissemination of ABHR.
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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 | 0.088 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.039 | 0.225 |
| Scholarly communication | 0.025 | 0.013 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.009 |
| 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; 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".