Bibliographic record
Abstract
Why do philanthropists create foundations? And why does the decision to do so matter?There are various motivations for engaging in philanthropy through a foundation: a wish for more personal involvement in the act of giving, the drive to involve family in a joint project, an appetite for risktaking and innovation in social change.But the first reason most foundation creators give is their desire to give back to the community and to society.They feel that their personal wealth was earned with the support of the communities in which they live and work.They are fortunate to be able to make a significant gift that will benefit many others.Samuel and Saidye Bronfman and their descendants exemplify this spirit of altruism.Their decision to create a foundation at a relatively early stage in the growth of the Canadian family foundation sector was a remarkable demonstration of that spirit.Yet what is it that makes a foundation more than an act of generosity with the added implication of longevity?The answer is sustained impact -on people, on ideas, on communities, on societies.The story of the Samuel and Saidye Bronfman Family Foundation is a story of impact.As described in these pages, the choices of Samuel and Saidye Bronfman and those who guided their foundation were consistently strategic.They made thoughtful and significant investments in their community and society, with results that far outweighed the dollars spent.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.568 | 0.470 |
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".