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Record W3119874210 · doi:10.5430/ijfr.v12n2p55

Impact of Covid-19 in the European Start-ups Business and the Idea to Re-energise the Economy

2021· article· en· W3119874210 on OpenAlexvenueno aff
Stavros Kalogiannidis, Fotios Chatzitheodoridis

Bibliographic record

VenueInternational Journal of Financial Research · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsScrutinyContext (archaeology)Coronavirus disease 2019 (COVID-19)Order (exchange)European unionPandemicEuropean economyEconomyBusinessEconomicsPolitical scienceMarket economyEconomic policyGeographyLawFinance

Abstract

fetched live from OpenAlex

This paper has been constructed with the aim to evaluate the profound Impact of Covid-19 in the European start-ups business as well as to illustrate certain effective ideas in order to re-energise the economy. The sudden arrival of this deadly pandemic has impacted the overall world in many different ways and economy is one of them. Therefore, through the execution of secondary research, this study is going to assess the distinctive notions of economic downfall in the context of European countries and different industries of this continent. In the last decade start-ups have generated a lot of employment. Start-ups have found new markets and opportunities that have revolutionised the way business has been perceived. The titans of commerce and various industries have been left behind by smart innovation and sheer brilliance. So it is only natural that there should be more scrutiny on the impacts of the Corona pandemic on the start-up industry. The impact of the Covid-19 pandemic on the start-up industry should be carefully studied in order to make more informed policies. The aim of this paper is to study the impact of Covid-19 on the start-up industry of the European Union. This paper will also make an in depth study of the strategic remedies employed by various governments re energise the sector.

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)0.013
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.133
GPT teacher head0.404
Teacher spread0.271 · 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

Citations21
Published2021
Admission routes1
Has abstractyes

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