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Record W4388190149 · doi:10.52673/18570461.23.2-69.11

Influence of the securities market on economic growth

2023· article· en· W4388190149 on OpenAlexaboutno aff
Ana Litocenco

Bibliographic record

VenueAkademos · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsnot available
Fundersnot available
KeywordsOrder (exchange)Third marketFinancial marketGovernment (linguistics)Security marketInvestment (military)Context (archaeology)BusinessState (computer science)Domestic marketInvestment bankingFinancial systemMarket economyEconomicsFinanceInternational trade

Abstract

fetched live from OpenAlex

The state securities market represents a reliable source of financing for the economy, which the state can call on, but also can develop, in order to obtain the necessary financial resources for economic development and growth by creating jobs, supporting investment activity, as well as carrying out all actions and measures planned in all spheres of the national economy. At the same time, a developed domestic government securities market can contribute to reducing the state’s dependence on external financial resources, as well as mitigating some of the effects of financial crises. In this context, the purpose of the given article is to analyze the role and influence of the domestic state securities market on the economic growth from a theoretical and practical perspective, being researched based on econometric models, the interdependence between the level and volume of the domestic government securities market of the Republic of Moldova, Brazil, Canada, Romania and Hungary and their economic growth. The results obtained, following the research, show the existence of an interdependence between the analyzed phenomena, the increase in the volume of the domestic securities market generating a certain growth in the economy of that country.

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)0.000
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 score0.809
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.189
Teacher spread0.181 · 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
Published2023
Admission routes1
Has abstractyes

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