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Record W4220763437 · doi:10.31857/s032150750019246-2

The anti-crisis policy of the Japanese government and Bank of Japan during the COVID-19 pandemic

2022· article· en· W4220763437 on OpenAlexaboutno aff
Denis Suslov

Bibliographic record

VenueAsia and Africa today · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicQuarter (Canadian coin)Coronavirus disease 2019 (COVID-19)Government (linguistics)Economic recoveryEconomic policyPopulationEconomicsBusinessFiscal policyDeficit spendingDevelopment economicsDebtMonetary economicsGeographyFinanceMacroeconomicsMedicine

Abstract

fetched live from OpenAlex

The parameters and the assessment of state economic policy measures to eliminate the economic shocks of the COVID-19 pandemic in Japan from January 2020 till July 2021 are carried out. It was revealed that the economic policy of responding to the shocks of the COVID-19 pandemic was timely and adequate to the current crisis. It was confirmed that in 2020, compared to other developed countries, it was possible to effectively use its health care system in the fight against COVID-19 and reduce economic losses in the initial stage of the pandemic. It is also confirmed that the package of anti-crisis measures turned out to be one of the largest in the world in terms of volume and that in the coming years its implementation will lead to a sharp increase in the budget deficit and public debt. It was found that timely measures of fiscal policy within the framework of additional budgets and monetary policy of the Bank of Japan, in general, led to the return of the Japanese economy by the second quarter of 2021 to a development trajectory with positive growth rates (about 2 per cent a year). However, the Japanese economic recovery remains fragile due to low rates of vaccination of the population and restrictions on the pandemic, which are holding back the activity of the private sector of the economy.

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 score0.730
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.023
GPT teacher head0.228
Teacher spread0.205 · 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
Published2022
Admission routes1
Has abstractyes

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