Policy Forum: Half-Siblings or Close Cousins? Contrasting Operating and Grant-Making Foundations Through a Disbursement Policy Lens
Bibliographic record
Abstract
Almost all charitable foundations in Canada are subject to a minimum spending rule, requiring them to disburse a set portion of their assets annually. According to 2022 amendments to the Income Tax Act, foundations must spend at higher rates if their assets exceed a new statutory threshold. While the size-based spending policy recognizes the need for sustained support of the charitable sector, it overlooks the diversity of Canada's foundation community. How can we make sense of these spending obligations for foundations that finance their own philanthropic initiatives (operating foundations) compared to those that solely provide grants (grant-making foundations)? This article takes a close look at the distinction between Canada's operating and grant-making foundations, using key measures from the Canada Revenue Agency's foundation data for the years 2005 to 2018. It questions how "related" the two categories of organizations are in the face of identical spending obligations. Additionally, this article clarifies the policy intention of the existing disbursement requirement, considering that Canada's foundation sector is becoming increasingly diverse and that operating foundations are gaining a stronger presence in this sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".