Future Use Architecture: Connecting housing policy, housing typology, and resource use for housing in Canada
Bibliographic record
Abstract
This study investigates the potential of residential building material stock in Canadian cities to address Canada’shousing and retrofitting needs. We introduce the concept of Future-Use Architecture (FUA) within a Circular Economy(CE) design approach. Cities are significant contributors to a nation’s material resource use, but they are also banksof materials. In alignment with Canadian government policies and projections, the study addresses the imperative ofretrofitting 600,000 homes annually until 2040 and meeting the demand for 2.3 million new homes between 2021 and2030. FUA involves incorporating recovered materials into new building designs and the early integration of end-of-life building strategies, such as design for disassembly. This approach encompasses a comprehensive evaluation of urban building material stocks and the development of reuse and recycling strategies.This paper builds on prior work by the authors that investigated the potential carbon emission reductions throughmaterial recovery in Canadian housing stocks. Taking this as a starting point, it links this knowledge to current government policies for renovating and building new housing in Canada by 2040. The findings highlight the substantial quantities of building materials embedded in our structures and the considerable potential for reducing environmental impacts, such as carbon emissions, through adopting the Future-Use Architecture (FUA) approach. However, it becomes apparent that substantial shifts in both material supply and construction practices within Canada are imperative to fully unlock the potential of FUA and efficiently utilize the materials stored in our buildings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".