Knowledge as Leadership, Belonging as Community: How Canadian Community Foundations Are Using Vital Signs for Social Change
Bibliographic record
Abstract
The concept of “community” in community foundations is being reframed – less strictly tied to the specific locales that originally defined their boundaries and increasingly about a process of engagement and a resulting sense of belonging. The greatest asset of a community foundation is not the size of its endowment, but its knowledge of community and ability to use this knowledge for positive change. This article explores the Canadian network of community foundations’ use of the reporting tool Vital Signs to implement a knowledge-driven approach to leadership and how it is using this knowledge in more inclusive, engaged models of community to drive change agendas in their own communities and, collectively, at a national scale. In implementing knowledge as a leadership tool, there remains a vast difference between what is feasible for the large community foundations and the small and new ones, particularly those in more isolated places. In spite of these constraints, community knowledge can become a means of scaling attention to particular issues and give many community foundations the confidence to frame issues in new ways.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".