Bibliographic record
Abstract
As climate change continues to pose a threat to human health, cities have turned to nature-based solutions, such as green infrastructure (GI), to lessen the impacts of climate change felt by communities. However, many practitioners are not incorporating equity considerations in GI siting decisions; thus, leaving marginalized and racialized communities to disproportionately bear the impacts of urban environmental issues. In the City of Vancouver, British Columbia, Canada, the GI Branch is investigating ways in which they can apply an equity lens to their work. To aid in their endeavour, this study examines existing challenges to equitably implementing GI, as well as areas for improvement, through a literature review, document and planning tool analysis, and key informant interviews. Drawing from the findings, this paper develops a set of equity criteria, which centre three dimensions of social equity: distributional, recognitional, and procedural equity, to help practitioners operationalize equity in GI project evaluations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".