Imaging a Sense of Place and Community:Curating Socially Engaged Art Interventions through Art-Based Action Research (ABAR)
Bibliographic record
Abstract
This dissertation investigates how innovative Socially Engaged Art (SEA) practices can cultivate a sense of community and place within rapidly transforming urban environments, focusing on Griffintown, Montreal—a historically post-industrial neighbourhood undergoing significant redevelopment. Employing Art-Based Action Research (ABAR) as its methodological framework, the study examines how art interventions can inspire social change and fortify community bonds. Three SEA interventions—a photography workshop, a collective mapping exercise, and a virtual open call—engaged diverse participants, fostering reinterpretations of Griffintown's spaces and cultivating connections among its evolving communities. Through iterative refinement and formative evaluations, the research deepened insights into the potential of SEA to foster meaningful community engagement. By fostering creativity, critical thinking, and dialogue, the interventions built a sense of place and community and contributed to the broader discourse on art's role in society. Findings highlight SEA's capacity to nurture a sense of belonging and collective identity, particularly in areas experiencing rapid urban transformation. The study emphasizes the role of socially engaged artists as curators and educators who facilitate dialogue, social integration, and cultural resilience. This dissertation advocates for curatorial practices that prioritize inclusivity, participation, and the empowerment of marginalized communities, proposing a model for curating SEA that bridges artistic, educational, and community-building endeavours. By addressing the intersections of art education, urban pedagogy, community engagement, and cultural policy, this work offers new frameworks for developing SEA interventions that promote cultural democracy and social change. This research's theoretical and practical contributions provide valuable insights for artists, educators, institutions, and policymakers, paving the way for inclusive, sustainable, and participatory pluralism in urban contexts.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".