An Analysis of COVID-19 Fiscal Policies in the US and Japan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In 2020, the COVID-19 disease created an unprecedented impact on the world. It created a health crisis in many countries, causing a pandemic. Along with the health crisis, most countries fell into an immediate economic recession including the US and Japan. This paper focuses on the fiscal policies used in the US and Japan due to the COVID-19 pandemic-related economic recession in both countries. First, a detailed analysis of the US and Japanese fiscal policies is presented, analyzing their effectiveness. Subsequently, these policies were compared and contrasted to obtain a better understanding of fiscal responses around the world. Overall, this paper aims to provide a new global perspective on the implementation of fiscal policies while also aiding policy-makers in making more educated decisions for future recessions caused by COVID-19 or other pandemics.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it