Urban Redesign of Public Spaces in Residential Neighborhoods to Raise the Efficiency of Coexistence in the Event of Dealing with Epidemics Case Study - City of Aqaba (Jordan)
Bibliographic record
Abstract
The epidemic following the spread of the coronavirus has led us to feel so distressed, as we have been forced to isolate ourselves -and in an instant we have lost the social relations between people.As the architect has the role of finding design solutions to ensure that in the event of an epidemic like the current coronavirus, people's lives don't have to undergo as many changes.This research has to study a block in an existing neighborhood in the city of Aqaba, Jordan, where it is shown for the lack of meeting spaces and entertainment spaces, people during the lockdown have suffered greatly.to prevent this from happening in the future, we have studied design solutions regarding free spaces in condominiums, to improve the social life of the inhabitants in the event of an epidemic.Naturally respecting the social distancing provided for by the laws of European countries and taking into account the recommendations of the World Health Organization (WHO).Interviews were made with the inhabitants of the neighborhood, a statistical and graphical analysis was carried out to find out the degree of acceptance of our design solutions.The result of the interview showed that 72.9% of the respondents strongly agree; 9.8% agree and 5.3% are neutral, while only 8.7% disagree and 3.3% disagree.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".