Provoking Points of Convergence: Museum and University Collaborating and Co‐evolving
Bibliographic record
Abstract
Abstract This article outlines art education courses undertaken in museum and gallery contexts as a component of the Certificate Programme in Visual and Material Culture within the University of British Columbia's Department of Curriculum Studies. With the creation of this programme and through the forging of relationships with area museums, unique ways have evolved for graduate students from diverse areas of education and art teacher education candidates to interact with works of art, museum professionals, artists, and the museum space itself. The purpose of these courses is to use museum and gallery settings as sites to test ideas, critique educational programmes, and advance new approaches for teachers to use museums in more creative and integrated ways in their teaching while expanding theoretical knowledge and interpretive repertoires. Through participating in this collaborative venture we have learned that when you invite teachers into museums, make efforts to increase their comfort within these spaces, while recognising what interpretive insights they offer as active participants in museum discourses, points of convergence between teachers, universities, and museums are formed.
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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 | 0.030 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.029 | 0.040 |
| Scholarly communication | 0.029 | 0.020 |
| Open science | 0.004 | 0.046 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".