Bibliographic record
Abstract
The global pandemic, COVID-19, has exacerbated the Gross Domestic Product (GDP) growth of the global economy since its outbreak in December 2019. One of the most affected economies, due to the global pandemic, is the US economy, currently crippled by an increased number of COVID-19 related deaths, layoffs, reduced work hours, and other related natural disasters, such as winter storms. Hence, it is imperative that the damage done to the GDP growth is evaluated meticulously to craft favorable monetary and fiscal policies to uplift economic performance. One of the key yet debated methods used by many economists is utilizing real GDP per capita as an economic performance measurement tool. Using two economic datasets and a multiple regression model, we compared real GDP per capita performance in the US economy between the second and third quarters of 2020. The study finds that the impact seems detrimental due to restrictions imposed on economic activities, such as business closures, disturbances in the supply chain, employee layoffs and reduced work hours. However, in the third quarter of 2020 COVID-19 after some of the COVID-19 imposed restrictions were lifted, the real GDP per capita significantly increased.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".