Nation, Race, and the Cultural Political Economy of Art for Social Change Philanthropy in Canada
Bibliographic record
Abstract
The arts are increasingly celebrated as a powerful mechanism for a philanthropic sector seeking to address some of society’s most pressing social and economic challenges. Over the past decade, “art for social change” [AFSC] charities and artists have become key sites of investment for some of the wealthiest philanthropic foundations in Canada and the United States. However, existing scholarship on the AFSC primarily serves as an exercise in mapping the field, or as a source of uncritical AFSC advocacy. In response, this dissertation provides the first critical exploration of the possibilities, tensions, and limits of the institution of AFSC philanthropy, specifically as it pertains to addressing contemporary issues of social injustice in Canada. This dissertation enriches the Marxian-inspired critiques of neoliberalism that dominate the critical literature on philanthropy by situating AFSC philanthropy more specifically within racialized and colonial histories of the Canadian nation-state. Offering a range of cases and combining methodological approaches including ethnographic study, discourse analysis, and analysis of public databases, this dissertation considers the manifold ways in which the intersecting dynamics of nation-building, racism, white settler-colonialism, and capitalist development articulate within AFSC philanthropy. This is accomplished by attending to the material aspects of racism and colonialism as they pertain to the capitalist accumulation strategies that serve as the economic engine of AFSC philanthropy. Equally important, however, are the ways in which discourses of Canadian identity—multiculturalism, diversity, tolerance, and international peacekeeping, for example—shape philanthropic policy, practice, and branding at the level of AFSC funder, community organization, and artist. Specifically, I argue that racist and colonial discourses of the nation and national “goodness” are mobilized by AFSC philanthropic funders, charities, and artists in ways that de-historicize and de-race the political-economic and cultural contradictions underlying AFSC philanthropy. Ultimately, this dissertation makes the case for centering questions of nation and race in studies of the AFSC, as well as in critical examinations of philanthropy more broadly.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.057 | 0.020 |
| Scholarly communication | 0.016 | 0.003 |
| Open science | 0.002 | 0.006 |
| 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".