Using Black Archives to Rethink Toronto’s Public Art: Integrating Community and Collective Heritage
Bibliographic record
Abstract
Using Toronto as our case study, this article examines the ways in which the city’s heritage industry may be improved by locating culture not only within the confines of a museum, but by engaging more deliberately with community-engaged artists whose work exists as both formal and informal public art. In Canada, Black people are underrepresented in the field of archives, and in the heritage sector, specifically museums, two institutions that play a significant role in the preservation, dissemination, and promotion of Canadian culture. This article probes the ethical question of why the selection process related to public art does not engage in dialogue with archives and the larger issue of how to preserve, protect and promote a diversity of interests, living spaces, and ecosystems. What is the potential value in collaboration between public art and the archive? What does it look like for public art to imagine the public sphere as an exhibition work rather than a static work of art? And in what ways does the archive hold the potential to disrupt colonial logics related to public art memory, storytelling, and heritage preservation? The authors argue that the archive is fundamental to knowledge sharing and that by thinking more deliberately about the efficacy of public art, we can begin to bring into conversation with people(s), place(s), and identities that are not traditionally thought of as fundamental to the notion of the “public” in public art. This article also examines the connection between Black Excellence and public art, arguing that the way some art exalts Black individuals over Black community is ultimately harmful to heritage commemoration.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.054 | 0.037 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".