The Best-fit Design of Future Homes in the Post-Pandemic Era: A Case Study in Iran
Bibliographic record
Abstract
The COVID-19 humanitarian situation is unparalleled, and there can be no return to the "old normal." According to reports, the COVID-19 Pandemic is now a significant public health issue that could have an influence on people's quality of life as well as environmental sustainability and social responsibility, as well as architecture and home designs. Bearing this in mind, this qualitative research was conducted to (1) to explore the extent to which the architects apply innovative strategies to fit the needs for the homes in the post-Pandemic era, and (2) to explore the architectural needs and wants of the residents in the post-Pandemic era. To fulfill these, semi-structured interviews were used to collect the data. A total of eight architects and ten residents living in apartment buildings were recruited by purposive sampling. The interviews were processed using thematic analysis. It was reported by majority of the architects that the most important innovative strategies to fit the needs for the homes in the post-Pandemic era are independent buildings, energy-related design changes, more human-centered design concepts, green open spaces, and advanced technology. As for residents, their architectural needs and wants in the post-Pandemic era were HVAC system, green open spaces, inappropriate designs, and lack of self-sufficient strategies. Further discussion on the findings have been reported in this research.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".