Bibliographic record
Abstract
Coronavirus disease-2019 (COVID-19) is predicted to have long-term consequences on the world's physical, mental and economic levels. June 2020 Global Economic Prospects describe the immediate and near-term impact of the pandemic, additionally the long-term injury to growth prospects. The baseline forecast predicts a 5.2% contraction in world GDP, despite the efforts made by governments to combat the downswing aided by business and financial support. Half the world's 3.3 billion personnel are threatened by the loss of their jobs. Moreover, workers within the informal economy are significantly vulnerable as a result of the lack of social protection, access to quality health care and productive assets. Individuals are finding it troublesome to survive through imprisonment as a result of a lack of resources to earn a living. According to Moody, the economic impact of the recent increase in COVID-19 cases is going to be restricted from April to June quarter, with a robust rebound within the last half of the year. The slower growth rate, on the other hand, can impede near-term economic recovery and have an impression on long-term growth dynamics. This study is focused on determining the critical effects of a Coronavirus pandemic on the global economy and anticipating the scenario that would confront the global economy soon. This research examines the various elements of Coronavirus and its economic implications.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".