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Record W4391261638 · doi:10.54097/8vzfv677

Chinese and Us Economic Policies During the Covid-19 Period: Analysis of Fiscal and Monetary Policies in the Two Countries

2024· article· en· W4391261638 on OpenAlexaff
Mingcheng Zhao

Bibliographic record

VenueHighlights in Business Economics and Management · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPeriod (music)Coronavirus disease 2019 (COVID-19)EconomicsMonetary policyFiscal policyChina2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Economic policyPolitical scienceMacroeconomicsVirologyMedicine

Abstract

fetched live from OpenAlex

During the ongoing COVID-19 pandemic, which is widely regarded as one of the most significant crises in human history, both China and the United States, being prominent global economies, implemented fiscal and monetary policies that were tailored to their own national contexts. Due to the substantial scale of their governmental structures and economic systems, conducting a comparative analysis of policy efficacy between these two nations has evolved into a multifaceted subject within the field of economics. The research aims to conduct a comparative analysis of the behavior and effectiveness of macro-stimulative fiscal policies in China and the United States, specifically focusing on their linkage with the monetary policies issued by the respective central banks. The primary objective is to examine how these policies contribute to the enhancement of economic vitality in both nations. In addition to analyzing the effectiveness of these policies, this study will investigate the adverse effects of these policies on the economies of the two countries.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.260
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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