Masking Visible Poverty through ‘Activation’: Creative Placemaking as a Compassionate Revanchist Policy
Bibliographic record
Abstract
Creative placemaking strategies are widely adopted by urban planners, local governments, and business communities in hopes to revive economically struggling urban areas. These strategies seek to attract pedestrian traffic by facilitating arts and cultural activities in underutilized urban spaces. While these are seemingly innocuous and uncontroversial urban design strategies, I argue that a particular creative placemaking tactic called ‘activation’ is a compassionate revanchist policy that is touted as a caring approach but, in practice, functions to mask visible poverty to advance a capital project in a revitalizing urban space. Drawing from a case study of a downtown revitalization project in London, Ontario (Canada), I show how a private placemaking consultant narrates and legitimizes this policy to city councillors as well as how the downtown business association rationalizes and enacts activation strategies. This demonstrates that private placemaking consultants are powerful actors who transfer and legitimize simplistic spatial solutions as a panacea to a local ‘urban crisis’. Activation does not resemble punitive tactics that exclude and criminalize homelessness, it rather aims to dissolve the homeless within the fabric of the revitalizing urban environment.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.074 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".