The transformative-learning potential of feminist-inspired guided art gallery visits for people diagnosed with mental illness and addiction
Bibliographic record
Abstract
Consciousness-raising practices at the heart of feminism remain one of the most vital components of transformative learning theory and provide the foundation for its constructivist underpinnings. Recently, there has been a call for educators to employ consciousness-raising practices outside of traditional classroom settings and to focus greater attention on the ‘extra-rational’ aspects of education – especially when working with marginalised adult learners. This is very much in keeping with engaged feminist pedagogy that allows space for emotions in learning. Reflecting on our experiences facilitating access to art programmes with people diagnosed with mental illness and addiction at the Art Gallery of Ontario, we highlight several examples of gender-related consciousness-raising that emerged. These experiences suggest that exploring mental illness through engagement with art in a social setting allows participants to deepen their understanding of art and the political implications of their life experiences. These tours also seem to serve as a powerful counterpoint to more clinical and masculine ways of framing mental health and well-being. Ultimately we argue that it is our explicitly feminist approach to transformative learning and not the existence of accessibility programmes themselves that holds the promise of redefining what and who museums are for.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".