Evaluating equity and justice in Vancouver’s Sea2City Design Challenge
Bibliographic record
Abstract
Like many coastal cities, Vancouver, British Columbia faces risks from future sea level rise and has begun coastal adaptation planning in the last decade. In 2021, the City launched the Sea2City Design Challenge (Sea2City), a sea level rise design challenge in False Creek, a narrow inlet bordering downtown Vancouver on the unceded territories of the xʷməθkʷəy̓əm, Skwxwú7mesh, and səlilwətaɬ Nations. The challenge brought together city staff, international design teams, Indigenous cultural advisors, youth, community representatives, and technical advisors to develop design concepts for adapting to a rising False Creek. Now complete, Sea2City offers opportunities to innovate coastal adaptation and evaluative research practices.Using Sea2City as a case study, this research applies the JustAdapt framework to better understand how equity and justice were incorporated into its process and outcomes. Developed by the study’s researchers, the JustAdapt framework is a new evaluative framework for practitioners and academics alike to bring accountability to their equitable adaptation work. Findings suggest that equity, not justice, was actioned through Sea2City’s process and engagement. While the challenge shifted to emphasise Host Nations and local ecology, less focus was placed on different knowledges and lived experiences.With climate change already disproportionately impacting equity-denied populations, scholars and activists call for climate justice. Coastal adaptation planners have identified equity and justice as important principles guiding their work, yet evidence on implementation and evaluation is lacking. This research contributes a case study on evaluating equity and justice in Vancouver’s Sea2City Design Challenge and hopes to inspire future evaluative projects and research.
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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 | 0.043 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.013 |
| Scholarly communication | 0.020 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| 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".