Catalyzing climate action: a case study on universities as hubs for community-engaged climate projects
Bibliographic record
Abstract
Purpose Academic institutions are in a unique position to secure funding and work with communities on climate change issues that matter most to them while providing research opportunities that benefit faculty and students. The purpose of this case study is to outline the steps to secure funding to create a grant program that prioritized partnerships; equity, diversity, inclusion and accessibility (EDIA); truth and reconciliation; and experiential learning (EL) opportunities while sharing lessons learned and recommendations to grow community-engaged climate projects at other academic institutions. Design/methodology/approach The University of Calgary placed community at the center of this grant program by connecting the program to university strategies; receiving guidance from a community circle of advisors and university experts in knowledge engagement and EDIA; and creating a grant application and evaluation process that reflected ongoing feedback. While not exclusively designed for funding projects using participatory methods, the grant program incorporated participatory approaches. Findings Participatory, inclusive and transdisciplinary approaches to the design and implementation of a community-engaged climate action grant program were key to its success. Originality/value This case study represents a unique approach to creating a granting program at a university that brings together community groups, faculty and students to realize climate action through honoring participatory, inclusive and transdisciplinary approaches.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.008 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".