Boom or bust: adapting 1960s, 1970s, and 1980s housing affordably and sustainably
Bibliographic record
Abstract
Canadian homes built between the 1960s and 1980s met the space and lifestyle needs ofthat generation. However, in the decades since then, social attitudes and demographics havechanged. These houses can be affordably adapted to be more sustainable as well as increase theirsuitability for current and future homeowners. Examining the changes that occupants havealready made to their houses over the decades can assist designers and policy makers in designingand introducing policies for easily adaptable dwellings.To gain information about possible needs and modification strategies, the authoridentified and selected a suburban neighbourhood built in the 1970s and 1980s. 225questionnaires were distributed in that one neighbourhood of 2,100 homes. 82% of thequestionnaires were returned and the responses were analysed. Seven follow-up interviews werealso conducted.The questionnaire responses indicate that homeowners modify their houses for theircurrent, but not future needs. Energy efficiency, sustainable energy generation, and resourceconservation systems were shown to be of low importance to homeowners, with the oneexception of replacing windows and doors. Respondents were strongly opposed to makingmodifications that could result in diversified, affordable housing and in densification of theirneighbourhood, for fear that it would have a negative impact on the suburban lifestyle they hadchosen.The author demonstrates that while the houses and the neighbourhood could be adapted toprovide more affordable housing options, and straightforward modifications could be made to theexisting houses in the research neighbourhood to increase efficiency and sustainability, thesechanges will not happen without changes to people’s perception of suburban living in the twentyfirstcentury. Additionally, people’s resistance to overt change, as found in this study, maychallenge current designers and policy makers in modifying current houses and neighbourhoods,as well as in designing easily adaptable dwellings to address people’s changing needs as afunction of time.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 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".