Bibliographic record
Abstract
Major philanthropic organizations are increasingly turning to the arts for social change [AFSC] to address racial injustices ranging from racialized poverty and mass incarceration, to health and educational disparities. This article problematizes the emergence, and increased celebration, of AFSC philanthropy by situating it within successive articulations of racial neoliberalism. Focusing on the Canadian context, I argue that this ‘progressive turn’ in arts philanthropy is the product of a series of neoliberal political-economic and ideological shifts that uniquely punish(ed) the racialized poor on a material level, while simultaneously producing these same communities and their artistic practices as attractive sites of investment for a primarily white and increasingly empowered philanthropic base. Drawing on a series of examples, I show how contemporary approaches to AFSC philanthropy – particularly those that mobilize ‘business-like’ strategies, priorities, and tools in pursuit of social change – function to extend and legitimize the ‘post-racial’ ideological foundations of the racial neoliberal project, resulting in a paradoxical phenomenon that I term ‘racial neoliberal philanthropy’. In addition to making the case for centering race in extant critical work on the political economy of philanthropy, this article – as well as the concept of racial neoliberal philanthropy – highlights how well-intentioned organizational responses to racial injustice can, in fact, reify racial inequities through policies and programming that are seemingly ‘beyond race’.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.055 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".