The community benefits of choosing grey over green infrastructure in planning the Rockcliffe Riverine Flood Mitigation Project in Toronto
Bibliographic record
Abstract
Green infrastructure (GI) is increasingly promoted for urban stormwater management, but its adoption remains limited. This paper investigates the Rockcliffe-Smythe Riverine Flood Mitigation Project (RRFMP) in Toronto, where a naturalised channel was eschewed in favour of a traditional, grey concrete channel. Through project document and interview content analysis, we find that the planning process unfolded through a fundamentally grey infrastructure framework, which prioritised technical feasibility, cost-efficiency, and stormwater conveyance over ecological and social co-benefits that would accrue to flood-affected communities. Infrastructure evaluation criteria excluded equity considerations and applied a loss-minimising lens that devalued GI’s additional co-benefits. Our contribution shows how political and institutional barriers to GI implementation that perpetuate traditional grey thinking and impede greening end up maintaining social and environmental inequities underlying flood vulnerabilities. We argue that integrating equity considerations, valuing co-benefits, and including multidisciplinary expertise can enable socially and ecologically just GI implementation for flood mitigation.
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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 | 0.006 |
| 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 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".