Planning for Housing and Food Security with Sustainable Design
Bibliographic record
Abstract
Despite the social and economic success in Canada, both housing and food are recognized as important issues that are without a comprehensive framework that identifies the intersections between the two. Currently, there is the National Housing Strategy, and the National Food Strategy that will be released in Fall 2018, but these initiatives and others like them may not be enough to tackle the dilemma that the City of Toronto is facing. The concentration of people that are unable to afford and access adequate housing and food challenges the future health of communities. Given that, this paper is a compilation of meaningful literature and dialogue with planners, officials, housing providers, food advocates, and stakeholders in Toronto. Using this, there will be a discussion of strategies and plans aimed at housing and food in Canada. This will provide context, and explore the case for sustainable design as a possible way for bringing the issues of housing and food together in a more integrated framework. This paper offers potential recommendations through literature review, case studies, and interviews, and will conclude by looking at programs that can help to manage these problems and change the experiences of people.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".