Community-engaged flood mitigation and ecological restoration on a university campus
Bibliographic record
Abstract
Abstract Urban flooding is a major climate change risk that can be mitigated with green infrastructure such as tree plantings and restored naturalised buffers along watercourses. The creation and maintenance of green infrastructure projects for climate change adaptation provides opportunities for community partnerships and programming with co-benefits for human well-being and biodiversity. Here we present findings of action research on flood mitigation and education that was implemented on an urban higher education institution’s campus in partnerships within the university and with local Indigenous peoples, the regional conservation authority, the municipal government, and the public library. Through participatory workshops, we applied bioengineering and mixed planting methods to restore a creek bank. Using a combination of oral storytelling, digital media, and visual art that integrated perspectives across disciplines and knowledge systems, we situated our creek restoration project within a framework of environmental justice, emphasising specific acts of stewardship to improve watershed health and advance reconciliation. We produced digital resources and outreach materials to disseminate lessons from the project into the community and to support similar flood mitigation efforts at local and global scales. Our project demonstrates how incorporating partnerships into the design and implementation of nature-based solutions can build cross-cultural ecological knowledge, forge important on- and off-campus relationships, and create place-based opportunities for students to take direct and measurable action on climate change.
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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 | 0.004 | 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 | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".