Philanthropy, Racial Justice Organizations, and the Political Economy of Accountability
Bibliographic record
Abstract
Prompted by the Black Lives Matter movement, as well as COVID-19’s deepening of societal disparities, philanthropic foundations in North America have increasingly claimed racial justice as a core part of their mission and grantmaking strategy. This study draws on the concept of racial capitalism to examine racial justice organizations” [RJOs] accountability relations towards their philanthropic funders. Drawing on interviews with leaders of 30 Canadian RJOs, we show how accountability relations towards white wealthy philanthropies tightly entangle RJOs in the political economy of racial capitalism, structurally undermining—both epistemically and materially—their capacity to promote racial justice. This analysis departs from extant accounts of RJO-philanthropy accountability relations which have focused on processes of depoliticization caused by RJOs’ financial dependence on philanthropies. More specifically, we argue that accountability relations towards philanthropies place RJOs in a unique “bind of double dispossession”. To obtain material resources from philanthropies that are partially redistributing wealth expropriated from the racialized communities they represent, RJOs are expected to meet the epistemic demands of philanthropies. In doing so, they reproduce the partitioning that legitimizes and fuels the accumulation of philanthropic assets under racial capitalism. This study advances the critical philanthropy literature by showing how the political economy of philanthropy and donor-grantee relations are reciprocally connected in ways that re-entrench the material and epistemic foundations of the racial capitalist social order.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.043 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".