The Margin of Elegance: \nHow the university art gallery provides vital space for \ncareful, inquisitive, and imaginative postsecondary learning.
Bibliographic record
Abstract
The university art gallery, its collection, programming and spaces, offers a unique place for multidisciplinary learning outside of a traditional classroom. McMaster University’s The Art of Seeing at the McMaster Museum of Art, teaches medical residents compassion and professional wellbeing, alongside observation and communication skills, and learned with art. Such programs exist throughout universities in North America and until now, none have measured the long-term effectiveness of visual art-based learning. This project revisits participants of The Art of Seeing who experienced the program almost a decade ago. Qualitative data was gathered through semi-structured interviews and coded for themes. Four themes emerged around the long-term efficacy of The Art of Seeing: it was a positive experience during residency training; that learning with visual art at the campus gallery was meaningful; it was considered by participants as vital in preparing for careers as physicians, and; the programme was held in high esteem because it is core curriculum. For university art galleries in Ontario, this study proves education programmes in the gallery can be a vital resource to faculty and students in the academy. Based on this knowledge, complemented further through information gathered from expert interviews, this project offers four innovations for the future of university art gallery education. Consideration for such innovations will provide an example for university art galleries to achieve their education goals, and to prioritize strategy in order to remain sustainable and relevant in our time of financial pressure and social change. What in-gallery learning opportunities nurture in future physicians—slowing down, looking carefully, and being consonant with their colleagues in their inquiry—is a reclamation of the Margin of Elegance, where physicians can provide true, focused, and compassionate care. Thinking beyond visual art-based education of visual literacy and move towards including imaginative thinking and futures literacy will leverage the unique and novel learning environment campus galleries are known for. This is a space where we can be inspired to speculate on what might be in order to make meaningful change in our time.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.028 | 0.004 |
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".