Review on the Positive and Negative Impact of Covid-19 Pandemic on Environment and Society
Bibliographic record
Abstract
This review search aims to show the positive and negative impact of COVID-19 on the all aspects of life such as environment, education, economy, politics, social life, and social media, and most importantly global human health and health services. particularly in the most affected countries such as China, USA, Canada, Italy, Spain, Germany, UK, Brazil, Mexico, India, and Iraq. In terms of the environment our search shows that there is a positive impact associated between measures and improvement in air quality, reduction of fossil fuel traffic pollutes, reduction in greenhouse gases (GHG) generation, clean beaches, and environmental noise reduction due to air traffic suspension. The negative impact was associated with aspects such as the reduction in recycling and the increase in waste, which was endangering the contamination of natural resources (water and land), in addition to air. Other negative impacts on reduction global economic activity. In terms of education, COVID-19 had a big effect in changing the education system from classroom to electronic learning. The COVID-19 pandemic has had far-reaching economic consequences beyond the spread of the disease itself and efforts to quarantine it. As this virus has spread around the globe, concerns have shifted from supply-side manufacturing issues to decreased business in the services sector. The pandemic caused the largest global recession in history, with more than a third of the global population at the time being placed on lockdown. ((Anon., April-2020) Health-wise it was the reason for the reduction of the world population due to the high mortality and death rate. This is expected to be carried on for unpredicted months perhaps a year until the right vaccine is in reach of every person in the world.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".