The Social and the Impact of COVID-19 on Social Behavior in Streets of Amman, Jordan
Bibliographic record
Abstract
Governments around the world enforced many restrictions according to the recommendations of the World Health Organization (WHO) and tried very hard to minimize spread of epidemic in their countries. One of these restrictions is on using of public spaces that led to create new challenges to think about how we design public spaces and the way of using the most dynamic nearby spaces around us such as streets. The main objectives of this research are to measure the impact of COVID-19 on behavior of local community in public street. And to what extend changed of social behavior in public streets to compensation the absence of public spaces, where they became a breathing space for locals in Amman, Jordan. Also to addresses these questions which are focused on how the local community deals physically with the COVID-19 situation? And what are the changes that are done in their behavior to entertain themselves during the COVID-19 pandemic? Researchers carried out an analysis by using a mixed used approach; qualitative and quantitative methods through executing a questionnaire and a field observation of the study area which is selected. In conclusion, the results of the study showed that activities of local residents have changed between in the lockdown of COVID-19 pandemic and beyond whereas there has been more demand on active lifestyles which is continue after COVID-19 pandemic as new behavior of local residents. although the physical quality of the street are not design to meet new behavior.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".