Artwashing Toronto's Planning Landscape: A Critical Analysis of Creative Placemaking & Public Art
Bibliographic record
Abstract
Artwashing is a process enabled by states and developers who use art as a tool to rebrand and market previously disinvested spaces to be primed for new rounds of capital investment. These aesthetics seek to humanize processes of gentrification and exclusion. These same aesthetics are co-opted from the same communities in which 1) development happens and 2) "banishment" occurs. As an under-explored concept in the literature, this research aims to develop a more comprehensive understanding of artwashing through public art, and determine if artwashing may be happening in the neighbourhood of Regent Park. This research contributes to a novel qualitative understanding of artwashing and its entanglement with creative city policies and revitalization projects. This work offers planners critical perspectives on public art projects that will help policy makers to implement more equitable public art projects in 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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.028 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".