How America Responds to the Inflation Caused by Covid-19
Bibliographic record
Abstract
The Covid-19 pandemic has brought global inflation. Billions of people have been confined to their homes for months, unable to go to work. As an example, in the United States, which has the largest number of Covid-19 confirmed cases, the impact of the epidemic on their economy cannot is a big problem. According to the research, many of them contain the development of inflation by the Covid-19, but few papers research talk about the response of inflation for Covid-19 in U.S. central bank and the company overall. This research could serve as a model for other countries facing inflation. Therefore, this paper will use the background of the United States to explore the causes of inflation, the central bank's strategy, and how inflation reflects the difficulties faced by American companies. This paper mainly uses literature, case, and data analysis methods. Based on this paper, it was found that the fundamental factors of inflation were the imbalance of supply and demand and the break of the supply chain. But because of the Federal Reserve’s initial miscalculation about the duration of the Covid-19, they switched monetary policy from loose to tight in order to curb inflation. And American companies Apple and Amazon are both facing rising costs, labor shortages, and supply chain disruptions, but their solutions are different because of changing consumer preferences.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".