Foundations in Canada: A Comparative Perspective
Bibliographic record
Abstract
From a Canadian perspective, this article provides a comparative historical and contemporary overview of foundations in Canada, in relation to the United States and Germany. For the purposes of this analysis, the study was limited to public or private foundations in Canada, as defined by the Income Tax Act. As the Canadian foundation milieu straddles the welfare partnership model that characterizes German civil society and the Anglo-Saxon model of the United States, Canadian foundations as a whole have much in common with the foundation sector in both countries. Similarities include the number of foundations per capita, a similar range in size and influence, a comparable diversity of foundation types, and an explosion in the number of foundations in recent decades (although the United States has a much longer history of large foundations making high-impact interventions). This analysis also highlights some key differences among larger foundations in the three jurisdictions: German foundations are generally more apt to have a change-orientation and are more vigorous in their disbursement of income and assets. U.S. foundations are more likely to play a welfare-replacement role in lieu of inaction by the state. Canadian foundations play a complementary role, particularly in the areas of education and research, health, and social services. At the same time, there is a segment of Canadian foundations that are fostering innovation, social and policy change, and are embarking on meaningful partnerships and acts of reconciliation with Indigenous Peoples in Canada.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.023 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".