Resilience Rainbow What Role Can Community Foundations Play in Increasing Community Resilience
Bibliographic record
Abstract
What makes a community resilient? Understanding the dynamics of a community can help it to best adapt and grow in the face of sudden or sustained challenges, be it a natural disaster or an economic crisis. Interest in community resilience is emerging in civil society, the social sciences, and within government. This paper examines the nature of what makes a strong community, and how community foundations can help increase resiliency in their local areas. The author forms the initial hypothesis that community foundations that undertake "community needs mapping" are expanding their roles in civil society beyond that of traditional grant maker. She uses selected case studies as a lens to examine community resilience and to look at the role the respective foundations play in these contexts. The author builds a resilience framework with seven elements, which comprise what she calls the "Resilience Rainbow", in order to explore the topic of community resilience. Her paper focuses on case studies - from Canada, the U.S., Brazil, Mexico and Slovakia - of seven community foundations which have recently undertaken "community needs mapping". In her findings, the author maps the themes of the "Resilience Rainbow" against those emerging from the case studies. The author goes on to analyse the differences in the foundations' roles and the potential reasons for these differences. She concludes the paper with a look at why and how certain community foundations? roles are evolving, with a focus on the ways their work has an impact on the resilience of their local communities.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".