Examining the COVID-19 Response of Canadian Grantmaking Foundations: Possibilities, Tensions, and Long-Term Implications
Bibliographic record
Abstract
This article explores how Canadian philanthropic foundations with social justice mandates responded to the social and economic impacts of the COVID-19 pandemic by loosening restrictions for grantees; collaborating on new initiatives; elevating grassroots knowledge; and balancing short- and long-term priorities. This response, however, revealed a series of tensions in the dominant pre-COVID-19 philanthropic model—specifically, as a mechanism to address the social, econ- omic, and ecological crises that predate COVID-19. The early pandemic response of grantmaking foundations can there- fore serve as a model for what a more democratic, agile, collaborative, and justice-oriented philanthropic sector can look like. RÉSUMÉ Cet article examine la réponse de fondations philanthropiques canadiennes aux enjeux de justice sociale pendant la pandémie de COVID-19. Elles l’ont fait en assouplissant les exigences exigées aux donataires; en collaborant autour de nouvelles initiatives; en priorisant l’expertise des communautés; et en équilibrant les priorités à long et à court terme. Cette réponse révèle les tensions inhérentes au modèle classique de l’action philanthropique, particulièrement dans les façons de répondre aux crises sociales, économiques et écologiques. La réponse actuelle fournit des bases solides pour repenser le modèle d’action du secteur philanthropique subventionnaire afin qu’il soit plus démocratique, plus collaboratif et plus axé sur la justice.
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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 | 0.022 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.034 | 0.020 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".