Resident-centric retrofits for social housing: A multi-solving approach
Bibliographic record
Abstract
Housing around the world needs to be retrofit to achieve our decarbonization goals. Social (or public) housing is a particularly critical sector given that it often serves priority populations who have limited choice in how they are housed. At the same time, social housing in many jurisdictions suffers disproportionately from poor performance that impacts the resident experience including substandard indoor air quality (IAQ), thermal discomfort and broader control issues, often due to underinvestment at the time of construction and throughout the building service life. Further, these performance issues will be exacerbated as our changing climate brings more extreme weather events, including heat waves and wildfires. By focusing retrofit goals entirely on decarbonization, the performance issues impacting residents often remain unaddressed, therefore a framework for considering post-retrofit building performance more holistically is needed. The concept of “multi-solving” retrofits, where multiple performance issues are addressed through a single project, presents a framework through which we can expand our consideration of retrofits beyond energy and carbon. These new directions include reducing life cycle carbon burdens and improving resilience to extreme events; housing affordability; health, comfort and control; and livability and community are described. By applying this framework, retrofit investments can yield benefits for residents that extend beyond initial environmental goals. Following an introduction to the framework and examples of how it can be applied, future directions for the research community, policy makers and industry are suggested to promote widespread adoption of resident-centric retrofits (where resident needs are prioritized) that address the multitude of challenges facing social housing globally. These directions include the need for better data collection on holistic retrofit performance, co-benefit valuation and decision support tools for the building industry. • Many social housing developments face multi-dimensional challenges. • Retrofit investment should achieve more than just energy and carbon targets. • Retrofits can be used to meet environmental, social and economic goals. • Multi-solving retrofits can improve affordability, resilience, comfort & community. • Need policy changes, co-benefit quantification and decision support tools.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.005 |
| 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".