Impact of Covid-19 pandemic on global output, employment and prices: an assessment
Bibliographic record
Abstract
The effects on human health and life have been catastrophic and its adverse economic effects have been profound, as many countries are engaged in lockdowns, social distancing, mobility, and other restrictions. These have caused unprecedented disruptions in production, distribution, consumption, supply chains, tourism, trade, investments, among others. This paper utilises a simple conceptual framework to anticipate the potential adverse impacts of the pandemic on the economy focussing specifically on output, employment, and prices. The anticipated potential outcomes on the above-mentioned global indicators are assessed utilising publicly available data and information. Both qualitative and quantitative information are utilised which are obtained from various primary and secondary sources. Consistent with the potential outcomes, the paper finds and reports severe adverse impact of the pandemic on global production, employment, and prices. Finally, the paper discusses policy responses to combat the crisis by governments and international organisations and discusses some business implications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".