A Connected Community Approach: Citizens and Formal Institutions Working Together to Build Community-Centred Resilience
Bibliographic record
Abstract
Urban resilience research is recognizing the need to complement a mainstream preoccupation with "hard" infrastructure (electrical grid, storm sewers, etc.) with attention to the "soft" (social) infrastructure issues that include the increased visibility of and role for civil society, moving from (top-down, paternalistic) government to (participatory) governance. Analyses of past shock events invariably point to the need for more concerted efforts in building effective governance and networked relations between civil society groupings and formal institutions before, during, and after crisis. However, the literature contains little advice on how to go about this. In this paper, we advance a Connected Community Approach (CCA) to building community resilience with a specific focus on the relationship between community and formal institutions. In the literature review that informs this work, we assess the current, limited models for connecting communities to formal institutions, as well as the emerging role of community-based organizations in this work, and we offer our own assessment of some of the key tensions, lacunae, and trends in the community resilience field. Principally, we explore the potential of the CCA model, as spearheaded by the East Scarborough Storefront and the Centre for Connected Communities in Toronto, Canada, as a promising approach for building the relational space between civil society and the state that is so often called for in the literature. The paper concludes with future directions for research and practice.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.010 | 0.044 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.004 |
| 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".