Policy Matters : grantmaking foundations and public policy engagement. A preliminary discussion on the Canadian landscape of grantmaking foundations and public policy engagement
Bibliographic record
Abstract
One can think of policy as the institutional acknowledgement and commitment to sustained action.In the context of foundations and societal change, policy is the difference between uncertainty that a change will take place and a collective commitment that it will.Foundations in Canada are in a unique position to impact societal change and foster social innovation.Unique because unlike most nonprofit and charitable organizations, foundations have an asset base, independent of government, that can be used, invested, or disbursed to support societal change through a variety of policy engagement tools.These policy engagement tools include direct action, legitimizing and supporting enabling strategies, and a range of funding and investment policies and practices.Funding practices can include grant-making, loans, loan guarantees, equity-type investments, and social impact bonds (Salamon, 2014).The purpose of this discussion paper is to present a theoretical framework that can speak to a) the bigger question of the relationship between Canadian grant-making foundations (GMFs), social innovation, and societal change; b) profile the issues that Canadian GMFs engage in and the tools they utilize at each of five stages in the public policy change process -whether at the municipal, provincial or federal level; c) to provide some examples of Canadian foundations that
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.031 | 0.018 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".