Placemaking Through Artistic Pedagogy: Advancing Anti-Oppression in Public Spaces
Bibliographic record
Abstract
This duoethnographic study explores how placemaking through the arts can serve as a pedagogical method to advance anti-oppression efforts in public spaces.It draws from two projects: graffiti workshops in Toronto's Graffiti Alley and the creation of a school-based antioppression garden.The authors examine systemic barriers, including exclusionary policies and institutional resistance.Through personal narrative and critical reflection, the study shows how public art can foster equity, amplify marginalized voices, and reimagine public spaces as inclusive platforms for learning.The findings offer insights for educators, researchers, and policymakers on the pedagogical potential of community-driven art to support transformative learning. The pedagogy of public art and placemakingPlacemaking is a process that transforms public spaces into inclusive environments that encourage community participation and belonging (Brain, 2019;Mardan, 2023;Simões, 2023).It serves as a pedagogical tool through the use of art, media, and cultural practices that question norms, invite dialogue, and raise awareness of social issues (Pérez-Izaguirre & Reglero, 2023).This process supports reflection on values and builds stronger relationships between people and their surroundings (Zorrilla & Tisdell, 2016).Traditionally, placemaking positioned planners and designers as the main decision-makers, with limited input from the public (Champagne, 2020; Mardan, 2023).Public art has challenged this model by opening space for marginalized voices and encouraging social change (Raposo, 2023).Graffiti and street art engage broad audiences and speak to injustice, bringing attention to those who have been excluded (Fransberg et al., 2023;Simões, 2023).Projects such as Women on Walls in Northern Ireland and Palestinian resistance graffiti show how art fosters agency and mobilizes communities (Peteet, 1996;Rolston, 2018).Research on public art has shifted focus from aesthetic qualities toward its impact on dialogue and social equity (Schuermans et al., 2012;Zebracki et al., 2010).Rahder and McLean (2013) explored how race and gender shape public space.The Toronto Public Art Strategy 2019-2029 reflects the tension between economic priorities and inclusive community involvement (City of Toronto, 2019).Some projects illustrate how public art responds to injustice.In Vancouver, an aerial blockade led by Indigenous artists highlighted social issues and encouraged collective reflection (Museum of Vancouver, 2021; Naut'sa mawt Tribal Council, 2020).These cases show how art in public space promotes civic participation and challenges dominant power.Placemaking depends on critical frameworks to support equity.Without intentional approaches, exclusion and displacement may occur, especially in neighborhoods undergoing gentrification (Sullivan, 2021).This study examines two projects, a graffiti workshop and an anti-oppression garden, to consider how public art creates opportunities for shared ownership of space and deeper awareness of social structures.In this paper, we reflect on our observations of how systemic control over space limits equity work.By analyzing our experiences alongside the sociopolitical conditions that shaped them, this study contributes to broader conversations about placemaking as pedagogy.Public art and community-based projects offer pathways for reimagining public spaces as sites of learning, resistance, and transformation (Fransberg et al., 2023;Schuermans et al., 2012;Pérez-Izaguirre & Reglero, 2023).
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.036 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".