The Aesthetics of Resistance in Latin American Art in Canada
Bibliographic record
Abstract
This dissertation examines contemporary art by Latin American diaspora artists in Canada, focusing on artworks that address trauma, displacement, feminist resistance, and anti-colonial perspectives in the provinces of Ontario, Quebec, and British Colombia. While drawing on extensive artists' interviews and exhibition histories, this dissertation intends to highlight the formation of an aesthetic of resistance specific to the diaspora in Canada. By referencing key artists such as Alejandro Arauz, Osvaldo Ramírez Castillo, Francisco-Fernando Granados, Claudia Bernal, Maria Ezcurra, Helena Martin Franco, Claudia Chagoya, Laura Barrón, Paolo Almario, and Monica Martínez, this study posits positionality as fundamental. The research also focuses on coalition building as a method of resistance with a close examination of the non-profit arts organization, Latin American Canadian Art Projects. Furthermore, recognizing "Latin American art" as a contested construct serving a geopolitical and economic purpose, this dissertation employs the term as a marker of shared displacement experiences and histories within the context of the diaspora. The various case studies highlight resistance and critical consciousness, illuminating the geopolitical and economic structures that marginalize diaspora artists. The featured artists are united not by origin but by their reasons for leaving Latin America and their resistance strategies within a Canadian context. What ties these artists together is not necessarily their culture, ethnicity, race or language. Rather it is their shared interest and commitment to resist hegemonic systems of power. Their work challenges and critiques oppressive regimes, and subverts imperial patterns, offering what I term an aesthetic of resistance. Drawing upon positionality and oppositional consciousness as key concepts, this research investigates issues of representation and erasure of Latin American art in Canada. Ultimately, this dissertation foregrounds the importance of coalition building and artistic renditions of resistance that respond to violence and oppression as a methodology and aesthetic. This knowledge offers not only historical insights but proposes a deeper understanding and contextualization of current phenomena of subjugation and how artists respond to oppression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.025 |
| Scholarly communication | 0.016 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".