Towards a Framework for Building Community-University Resilience Research Agendas
Bibliographic record
Abstract
In this paper, we ask: “How can we scope multiyear, multiscalar community–university collaborations that draw on the university’s diverse resources and contribute to community resilience”? We approach this question by presenting the development and application of the Advancing Collaborative Transdisciplinary Scholarship Framework (the “ACTS Framework”) which we argue has been successful at helping us better understand, foster, and work towards communities’ resilience. The ACTS Framework, informed by our collective expertise in critical community-engaged scholarship (CES) and community resilience, contributes to knowledge and practice in critical CES, in particular by providing guidance for scoping and sustaining complex community–university collaborations. The structured yet iterative process involved in the framework development and application affirms and extends the work of other scholars interested in the links between CES and community resilience. Our contributions offer two other important practices—centring community concerns and facilitating cross-project collaboration—to critical CES knowledge and practice and highlight two promising practices of linking structures that facilitate community–university collaborations—specifically, a well-organized institutional memory and holding and bridging relationships.
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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 | 0.098 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.019 | 0.011 |
| Science and technology studies | 0.017 | 0.060 |
| Scholarly communication | 0.027 | 0.039 |
| Open science | 0.008 | 0.030 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".