Residents ask us 'Why is the living room so cold?' A systems approach to mapping retrofit decision-making among non-profit housing stakeholders in British Columbia and Saskatchewan, Canada
Bibliographic record
Abstract
Retrofit and repair of social and affordable housing can improve living conditions and reduce residential Greenhouse Gas (GHG) emissions. Increasingly in Canada, policies and programs aim to improve the condition and energy-efficiency of these aging buildings which have substantial deferred maintenance. This can mitigate the effects of climate change while preserving affordable housing for the growing number of Canadians in housing need – both critical for transforming unsustainable housing systems. However, policy and program effectiveness are constrained by complex and fragmented urban social housing systems, including overlapping jurisdictions and diversity of actors, policies, regional climates and energy sources.<br><br> Within this context, non-profit housing providers make daily asset management and maintenance decisions to best provide safe, affordable, and accessible housing now and in the future – decisions which may lead to major energy efficiency retrofits. However, retrofit decision-making is a process of negotiation between actors and materials evolving over several stages. To design more integrated and transformative policies, we must understand system elements currently shaping retrofit decision-making processes among these key system actors. Using a survey of non-profit housing stakeholders in contrasting Canadian community housing systems in British Columbia and Saskatchewan, followed by focus groups among Saskatchewan stakeholders, 16 influencing factors are identified and mapped to four early stages of retrofit decision-making, creating a process systems map. Factors vary in influence across stages; however, three – building issues, funding availability, and impact on residents – appear to drive the process. Through document analysis and interviews, influence of local system characteristics in each jurisdiction are then explored, informing policy and program recommendations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".