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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".