Creating Learning Communities: an analysis of public events at the Art Gallery of Ontario and the Toronto Biennial of Art
Bibliographic record
Abstract
This major research paper (MRP) analyzes the potential of bell hooks’s notion of learning communities within the context of contemporary arts institutions in the city of Toronto. It considers how two public programs—the roundtable discussion "Ways of Caring" at the Art Gallery of Ontario and the public gathering by BUSH gallery, "Beach(fire) Blanket Bingo Biennial", presented by the Toronto Biennial of Art—created learning environments that engaged participants in critical thinking, dialogue and self-reflexive practice. In doing so, each event challenged the colonial impositions and constructs of the host institution while subverting the structures that exclude racialized communities from their narratives. The MRP examines the diverse means through which learning communities take form, following three categories of analysis: ritualistic impositions, as discussed by Carol Duncan; participation and collective agency in relation to the writings of Claire Bishop, Pablo Helguera, and Irit Rogoff; and lastly, learning communities, as articulated by bell hooks. The essay ultimately seeks to prove that, by engaging in radical pedagogical approaches, museum education and discursive programs can challenge the institution’s colonial histories and structures by prioritizing and amplifying the voices of BIPOC communities.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.034 | 0.020 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".