Towards regional interdisciplinary green infrastructure in Metro Vancouver
Bibliographic record
Abstract
Green infrastructure (GI) and nature-based solutions (NbS) have been identified as an important strategy to assist in delivering key infrastructure services in Metro Vancouver, particularly when considering predicted and observed climate change impacts such as increased extreme weather, flooding, sea level rise, and urban heat for the region.Municipalities within Metro Vancouver are increasingly planning and deploying GI, though efforts are largely disjointed and are primarily planned and executed at the local government scale.Recent global initiatives to address biodiversity loss and climate change are recommending more integrated governance that incorporate planning between jurisdictions and disciplines highlighting the potential to achieve greater collective benefits including ecosystem services, biodiversity protection, and human health and wellbeing.However, a transformation to more integrated work is challenged by a variety of complex structural, cultural, and conceptual barriers common of wicked social-ecological problems.This research deployed social innovation techniques to engage professionals and stakeholders within the Metro Vancouver area to identify these barriers and reflect on potential solutions to deploy GI more intentionally and effectively at a regional scale.The results of the research demonstrate a strong preference towards greater integration between professions as well as between municipalities and governmental jurisdictions.
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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 | 0.005 | 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 | 0.012 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".