Designing for unhoused people: An inclusive public space strategy
Bibliographic record
Abstract
This project explores how landscape design can support social equality in the urban scale landscape after the Covid-19 pandemic. The pandemic totally changed the human lifestyle. It has also exacerbated spatial injustice within the urban fabric and among different socioeconomic groups. It has brought to the fore questions, such as What is the role of landscape/public space in mitigating the spatial injustices in the city? Furthermore, how can landscape design contain social care to design public space in a neighbourhood that includes large numbers of people who are experiencing homelessness? The Oppenheimer neighborhoods in Vancouver is one of those areas where the pandemic has had a significant impact, particularly on the homeless population. As such, this project will focus on interventions within the vacant lot, streetscape, and parking lot in an attempt to shift urban design focuses to include all users of the urban realm, including the homeless. Proposed designs demonstrate how flexibility, durability and inclusiveness can improve the wellbeing of the homeless communities through a healing garden, multi-use plaza, modular streetscapes etc.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".