St. George Rainway: Integrating Environmental Education into Public Engagement
Bibliographic record
Abstract
Green infrastructure (GI) is an emerging approach to rainwater management. As a result, few members of the public understand the functionality and designfunctionality, design parameters and co-benefits of these systems. This presentation addresses a key challenge to meaningful engagement of GI: how to integrate environmental education into public engagement and provide members of the pubic the tools required to provide informed feedback. In 2019, the City of Vancouver adopted the Rain City Strategy, a 30 year plan to change how we manage rainwater using green infrastructure. Through the City’s GI engagement work, the GI project team seeks to educate to the public on GI design opportunities (and limitations) to actively participate in the design process and create buy-in for its projects. Educational goals include: • Communicate that GI is more than just an ‘urban garden’ and provides essential utility service outcomes • Address lack of knowledge around urban stormwater pollution and green infrastructure solutions • Balance the desire for use of native plants or non-native with the need of plants that are suited to GI • Address concerns and misconceptions about water ponding water ponding, and water levels and maintenance in GI systems. • Balance the desire for use of native plants or non-native with the need of plants that are suited to GI • Manage expectations regarding ecological restoration and habitat creation. Using the City’s St. George Rainway project as a case study, we will illustrate how to integrate environmental education into public engagement. The St. George Rainway project included a multi-phased engagement, which leverages a breadth of education and engagement tools, including: • Virtual reality site tour • School engagement • Partnerships with local universities • Advisory committee • Educational materials, info sheets, factsheets, videos • Citizen science • Marketing based approach to engagement
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".