A Never‐Ending Painting: The Generosity of Time Spent Making and Learning with Others through Artistic Research
Bibliographic record
Abstract
Abstract This article examines the role of spending time with others in and through artistic research and practice. I draw from my doctoral work which took me on a cross‐Canada journey visiting 125 artists in their studios. Following the studio visits, I made a series of paintings of artists’ studios, however a year later these same paintings were cut up and rearranged to create collaborative studio assemblages on the walls of the Tate Exchange Gallery in Liverpool. Drawing on the metaphor of a never‐ending‐painting to examine never‐ending pedagogies, this article examines the evolution of this project through three iterations of the studio paintings. With each iteration, I explore different ways of knowing others through making thus proposing the performative and relational qualities of artistic research. The first iteration allowed me to spend time with artists even in their absence, as I engaged with our conversations through painting their studios, thus blurring the lines between solitary and social art practices. The second iteration allowed me to give up my art to others through asking them to create collages with fragments of my studio paintings. And the third iteration allowed my work to merge with other arts‐based researchers. Through this process, I propose that making art allows for multiple conversations to emerge through spending time getting to know others through art making.
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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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.037 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".