Community Foundations as Agents of Transformational Change: Lessons for Fondazione Caterina Dallara (Italy)
Bibliographic record
Abstract
Research and practice show that community foundations are well positioned to address controversial issues and take risks. Fondazione Caterina Dallara is a newborn community foundation in the Ceno valley of Italy with the mission to promote the social and cultural growth of the territory and its community. This paper addresses some of the challenges in the region and how they can be resolved by leveraging existing resources. In working in the area, Fondazione Caterina Dallara has carried out a community needs analysis, started the design of its headquarters, supported several civil society organizations through small grants, and sponsored a student exchange program. Using a mix of case studies illustrating the importance of strengthening civil society organizations, increasing youth participation, and utilizing the role of the space as community builder, this study draws from a wide geographic spread including Mexico, Brazil, Uganda, Northern Ireland, Canada, Armenia, Bosnia and Herzegovina, Switzerland, India, and Ukraine. The research presented in this piece points to new, creative, and flexible ways to solve social problems in relation to one another and through a participatory approach along with the community. Recommendations for community foundations include taking on a knowledge-driven approach, inhabiting the role of communicators, bridge builders, and advocates, as well as prioritizing networking with other community foundations.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".