The Effect of COVID-19 on Academic Social Life in Riyadh with a Focus on the Outdoor Environment
Bibliographic record
Abstract
On 18 March 2020, the World Health Organization announced that the coronavirus disease 2019 (COVID-19) pandemic had reached global pandemic status. The Ministry of Health in Saudi Arabia implemented a COVID-19 lockdown that lasted for four months. After the period of restrictions ended, people were supposed to return to their normal social lives; however, the lockdown had a psychological impact on people without them being aware of it. This research aimed to study the effect of COVID-19 on social life, mainly focusing on six public activities: visiting shopping malls, mosques, open spaces, interior space, psychological effect, and occupational aspects. The Method survey was distributed during lockdown including the six focus areas and collected using Google Forms. Also, a computer program simulation (ENVI-MET) was used to study and develop an outdoor environment. The research focuses on the outdoor environment to find solutions on a sample used Al Rouda Park in Riyadh. The results demonstrated that people are slowly returning to their social lives during the COVID-19 pandemic by steadily visiting shopping malls, mosques, and open spaces and half of respondents stay at home fearing COVID-19. The research concluded that people should apply health procedures during ongoing time in studied locations and should manage the elaborated psychological effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".