Creating Artist Citizens: Transformative Movements in Art Education and Programming
Bibliographic record
Abstract
Increased professionalism, a powerful art market, and the technological revolution are begging for an overhaul in arts education and museum programming. This paper provides insights into alternative approaches that have emerged over the last fifty years and hints at the urgency for future iterations. During my time as an art professor, I implemented alternatives to today's art schools, such as field schools, free schools, and ek-stitutions. From fourteen years working in Mexico, I learned of social and cultural movements like rural boarding schools, the Free School of Sculpture, and the Intercultural Documentation Centre. Back in Canada, when I was working as a museum educator, my programs were influenced by concepts such as educational turns, participatory programs, museum hacking, and the social work of museums. This paper presents transformative movements in arts education and programming, each with the goal of creating artist citizens ready to participate in a global society.
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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 | 0.012 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.052 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".