International comparisons : analysis of trends and issues from the International grantmaking sector
Bibliographic record
Abstract
This literature review seeks to contextualize the Canadian foundation grantmaking milieu with respect to international examples, with particular reference to social innovation. It provides an overview of comparative contexts that either (1) resemble Canada or (2) are distinguishable from it, to generate a set of best practices relating to investments in social innovation by grantmaking foundations – both through grants to other organizations and via direct programming. In order to highlight a diverse range of socially innovative forms and practices, we examine best practices in grantmaking in two broad contexts : First, in three countries that share a similar history and institutional landscape with Canada (United States, United Kingdom, New Zealand), and second, in three countries where grantmaking has developed along very different historical trajectories and where it has taken on different institutional forms. France, Sweden and Italy are home to unique foundation forms and grantmaking practices that differ in the services they provide, the laws that give rise to their respective organizations, and the core principles that shape their practices. The review is divided into three parts. In the introduction, we provide a context for our review, define grantmaking in the two broad contexts described above, and offer a definition of social innovation broad enough to capture these diverse contexts and approaches. In part 1, we describe the institutional forms and structure that grantmaking takes in the countries selected in our review, asking which of these forms can reasonably and usefully be extended to the Canadian context. Here we also touch on various features such as sector size, history, culture, assets, legal barriers, and international grantmaking, and explain the basis upon which countries are grouped together as similar or dissimilar. Part 2 examines social innovation in grantmaking through the use of individual country case studies that illustrate how grant-makers are applying novel techniques and strategies of grantmaking with the intention of having a greater social impact. This includes consideration of strategies such as using alternative community finance or investment models, scaling up programming, collaboration, pooled grants, cross-sector partnerships, technology and data sharing, the use of impact metrics, and/or grantmaking across international borders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".