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Record W4385234879 · doi:10.5430/bmr.v12n1p48

Cost-Efficiency Evaluation into the European Union Budget

2023· article· en· W4385234879 on OpenAlexvenueno aff
Antonio Sánchez‐Bayón, Fco. Javier Sastre Segovia, Ricardo Vega

Bibliographic record

VenueBusiness and Management Research · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
FundersUniversidad Rey Juan CarlosUniversidad Internacional de La Rioja
KeywordsEuropean unionEconomicsEconomic policyEuropean integrationInternational tradeBusinessInternational economicsEconomyEconomic system

Abstract

fetched live from OpenAlex

This review is a qualitative study, according to the heterodox approaches, assessed the leading beneficient State under the European Union budget system. For this purpose, the study utilized the Multiannual Financial Framework reports between 2014-2020, which dealt with more than 700 indicators, measuring success against more than 60 general objectives, and more than 220 specific objectives in the performance framework. Therefore, trade globalization, political economy, economic history, social contribution by the European Union budget, and international economics under new-institutional economics have been evaluated in this process. Assessment outcomes found that poorer Member States are the net beneficient regarding the European Union budget’s net balance and financial corrections. However, Germany is the leading beneficient of the European Union budget system as it has extended trade to all along the border of the European Union without tariffs and has become a more cost-effective economy against the outside economies (like the United States of America or China) of the European Union territory. Thus, the current study suggested that nations under the European Union budget system should invest more in sustainable production to get real-time benefit from the European Union integration.

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)0.017
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.866
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.239
GPT teacher head0.469
Teacher spread0.230 · 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 designOther design
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
Published2023
Admission routes1
Has abstractyes

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