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Record W4311299312 · doi:10.37075/isa.2022.4.03

Impact of the COVID-19 Pandemic on the Economy and the Banking Sector of Bulgaria

2022· article· en· W4311299312 on OpenAlexaboutno aff

Bibliographic record

VenueEconomic and social alternatives · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsPandemicCoronavirus disease 2019 (COVID-19)Presentation (obstetrics)Economic sectorPosition (finance)Quarter (Canadian coin)BusinessEconomic impact analysisCoronavirusEconomyDevelopment economicsEconomic policyEconomicsGeographyFinanceMedicine

Abstract

fetched live from OpenAlex

The goal of the research is to analyze the consequences caused by the coronavirus pandemic COVID-19 on the economy and the banking sector of Bulgaria. In structural terms, the research consists of an introduction, presentation, conclusion and cited sources. The significance of the researched topic is brought out in the introduction. The presentation analyzes the consequences caused by the COVID-19 pandemic on the economy of Bulgaria. It also analyzes the effect of the impact of the unfavorable macroeconomic environment caused by the coronavirus pandemic on the banking sector in our country. As we know, the stability of the banking system in a country is of extreme importance, because due to its structure-determining position, the banking sector is a key contributor to the dynamics of the economic development of the EU, including Bulgaria. The presentation of the article also describes the economic measures that have been taken with a view to reducing the adverse consequences of the new coronavirus. In the conclusion, the main implications of the research are presented, and the possibilities of using different policies are considered, with a view to mitigating the negative impact on the economic measures in the last quarter of 2022.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.998

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.032
GPT teacher head0.254
Teacher spread0.222 · 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.

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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