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Record W3014813200 · doi:10.5539/ass.v16n4p74

Assessing PIIGS Country Performance against Themselves and the EU

2020· article· en· W3014813200 on OpenAlexvenueno aff
Samuel D. Barrows

Bibliographic record

VenueAsian Social Science · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsDebtForeign direct investmentCurrencyEconomicsReal gross domestic productFinancial systemEu countriesGovernment debtInternational economicsBusinessFinanceMonetary economicsEuropean unionMacroeconomics

Abstract

fetched live from OpenAlex

This study reviews research and provides discussions on various aspects of optimal currency areas, the link between debt and growth rates, and government debt levels for the PIIGS countries which consist of Portugal, Ireland, Italy, Greece, and Spain. Ten years after the Great Financial Crisis (GFC), and five years after 2013, the year of peak debt levels to GDP for the PIIGS countries and the year of the lowest real GDP levels between 2011 and 2018 for the PIIGS countries, this study provides an assessment of PIIGS country performance relative to each other and to the EU. The study time frame includes the years 2013 and 2018 using twelve measurements grouped into four sections which provide insight into the economic performance of the PIIGS countries. The sections are Trade Flows, Industry / Debt / Foreign Direct Investment (FDI), Demographics, and Economic Outcomes. Based on a summary analysis of the measurements, the overall ranking is: Ireland, followed by the EU, Spain, Portugal, Italy then Greece.

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)0.002
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.038
GPT teacher head0.239
Teacher spread0.201 · 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
Published2020
Admission routes1
Has abstractyes

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