Adaption and Satisfaction Level of Housing Environments During Coronavirus Curfew in Jordan
Bibliographic record
Abstract
The global epidemic, evidenced the design gap formed in architecture where COVID-19 changed the way people live, induced designers to see how the epidemic affects people's daily habits in the built environment in the long-term life within a new framework e and vision of redesigning houses with the below perspective. This paper aims to discuss the design gap in pre-epidemic house design and how COVID-19 affects the relative parameters, focusing lost and unused spaces. Hence the suggestion stems from the need for providing solutions to the various crises that occur in the world, in order to contribute to a new urban life. The impact on houses that reflects the way of live in the community focusing on the mid-size apartments redesigning and creating new single spaces that combine several services for people needs during the pandemic COVID-19 lockdowns or self-isolation. This research is based on data collected during coronavirus curfew to analyze the satisfaction and adaptation of the housing environments design at psychological and physical levels in Jordan. Mixed-methodological approach is used for gathering information as well as getting a deeper understanding regarding the quality of housing environment from two perspectives: dwellers’ and housing designers’ perspective. The used methods are questionnaire that executed by using an online survey application and focus group executed via online meeting because of the COVID-19 lockdown. The data analysis showed that the architects’ role in both academic and professional fields should collaborate to raise awareness regarding the importance of creating high quality environments beside giving value to aesthetics. As well, focusing on humanistic aspects through adopting design approach derived from adaptability and flexibility to meet the dwellers’ needs in different situations such as a COVID-19 lockdown.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".