Housing Horizons: Models for Real Estate and Community Investment
Bibliographic record
Abstract
Toronto’s housing system is in crisis. As we persist in maintaining this failing system, we are limiting ourselves to the possibility of creating transformational change. Toronto’s housing arena is a complex organism of competing interests and influences, reinforcing a stratification between those who benefit from it and those who do not. With limited housing choices, many Torontonians are left with few opportunities to invest in their communities and to generate personal financial wealth for their futures. Through foresight methods, systems analysis, and generative design research techniques, this project asserts that we can create change in Toronto’s housing system by transforming real estate investment into an inclusive community-building tool. Housing Horizons begins by describing the evolution of the housing arena in Canada and analyzing the dynamics at play in the current system. The research then proposes several design principles for innovation: shift the power in the development industry to smaller community-based players, create wealth-generating mechanisms suitable for renters, and foster collaboration across stakeholders in the system. A city where all citizens can thrive is only possible when the housing system contributes to the wellbeing of its entire population – this vision can be realized through strategies that level the playing field for all.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".