MétaCan
Menu
Back to cohort
Record W4313071792 · doi:10.55365/1923.x2022.20.24

Analysis of Aspects of the Regional Economy in the Digital Economy, Using the Example of Financial Services

2022· article· en· W4313071792 on OpenAlexvenueno aff
Stanislavs Buka, Andrey Surmach, Oļegs Černiševs

Bibliographic record

VenueReview of Economics and Finance · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsDigital economyFinancial servicesGoods and servicesProduct (mathematics)Service (business)Point (geometry)Relevance (law)Information economyEconomyBusinessWorld economyEconomicsField (mathematics)FinanceComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The relevance of the study is that, in the aspects of the development and offer of financial products are not manifested outside the digital world, then from the point of the need for analysis, the question of the possibility and necessity of applying the existing approaches of the regional economy. raditional regional economic theories of the 20th century can be summarized as made-here-sold-there goods. The aim of the study is to analyse the offer of electronic financial services, as part of the Digital Economy, from the point of view of the regional economy. Research methodology includes analysis and synthesis of financial market knowledge. Financial services as an example were chosen by the authors since such services are completely provided in the digital field and thus will exclude the influence of aspects of the product or service that are provided outside the digital world on the final judgments and analysis. The paper discusses models for individual cases in the regional economy, when all regions are equally distant from each other. It is determined that a modern financial product can be based on local industries located in different regions, but united by one production -the so-called ecosystem, which has been studied at the international level.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.899
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.215
Teacher spread0.190 · 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 designTheoretical or conceptual
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

Citations9
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueReview of Economics and FinanceSame topicFinTech, Crowdfunding, Digital FinanceFrench-language works237,207