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Record W3167992875 · doi:10.33920/vne-04-2105-03

Evaluation of the effectiveness of integration processes of regional integration associations (on EU EXAMPLE, NAFTA, EAEP)

2021· article· en· W3167992875 on OpenAlexaboutno aff
V. G. Iordanova, Maria Shapor, Ksenia Petrovna Altukhova

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Development and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsRegional integrationEconomic integrationEuropean unionWork (physics)Member statesEconomicsInternational tradeUnemploymentInternational economicsMacroeconomicsEngineering

Abstract

fetched live from OpenAlex

This article is devoted to the analysis of possible scenarios for the development of the leading regional integration associations: The European Union (EU), the North American Free Trade Area (NAFTA) and the Eurasian Economic Union (EAEU) based on the study of the export dynamics of the studied groups of countries. The choice of the designated integration associations is due to various stages of the integration processes of the studied groups of countries. As you know, the EU is the only integration association that has gone through all the stages of integration interaction. In turn, the peculiarity of NAFTA is that there is a gradual decrease in tariffs in the implementation of trade between the member countries (USA, Canada, Mexico). Note that the designated association does not regulate the trade of member countries with third countries. In turn, the EAEU is the youngest and most dynamically developing integration association. The novelty of the approach presented in the framework of this work lies in the proposed methodology for predicting the dynamics of export volumes of the studied groups of countries based on an assessment of the dynamics of the following macroeconomic indicators: GDP, PPP, inflation, and unemployment. The essence of the methodology presented in the framework of this work is as follows: at the first stage, the choice of macroeconomic indicators necessary for conducting the appropriate analysis is carried out. The next stage consists in forecasting the dynamics of the selected macroeconomic indicators for the period chosen by the authors using the growth curve models. The final stage in the framework of the presented methodology is the compilation of the corresponding regression equations using the indicated macroeconomic indicators. In turn, the result of the research carried out within the framework of this work is the analysis of development scenarios for each of the studied integration groups using the author’s methodology, which is based on a combination of the use of growth curve models and the method of regression analysis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.115
GPT teacher head0.262
Teacher spread0.147 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2021
Admission routes1
Has abstractyes

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