The St. George Rainway : building community resilience with green infrastructure
Bibliographic record
Abstract
This thesis was written with cognizance of humanity’s passage into the current epoch: the Anthropocene. Impacts the human species currently have upon the biosphere are physically and chemically altering it to degrees that have crossed thresholds of sustainability. The side effects of this call for resilience to enable healthful transitions into the unstable and unpredictable future. Central to global climate change and central to the profession of landscape architecture is the element: water. The St. George Rainway offers a new opportunity to be a demonstration project for the City of Vancouver where the City and the community of Mount Pleasant act as collaborators with design, construction, and maintenance of a project with water in the public realm. There are three components essential to this work: a community survey, a series of stakeholder and expert interviews, and a design-application of theory using landscape architecture. The survey and interviews establish the local context for the thesis, a baseline for presence of social cohesion, and a framing for the applicability of the ten prominent characteristics of resilient communities. Taken together, the application of design responds to the collective voice and needs of the community and provides a set of goals, phases, strategies for design as a framework to help realize future implementation of the St. George Rainway. The voluntary engagement in the physical transformation of one’s community can provide opportunity for a growth in social cohesion. Subsequently, this growth can improve the conditions that fostered the bonds and bridges within that community that inspired the initial voluntary engagement. Green infrastructure, when considered through this lens, has a reciprocal relationship with social cohesion, where the improvement of one feeds into the improvement of the other. This model could therefore provide both a resilient option for physical development of land and for social development of community for a neighbourhood like Mount Pleasant by encouraging more interaction among neighbours and with the local public realm. The St. George Rainway: Building Community Resilience with Green Infrastructure aims to provide a framework for this.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".