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Record W2260817148

Economic Growth, Employmentand the Crisis in Europe

2015· article· en· W2260817148 on OpenAlexaboutno aff
Sacala Cristina

Bibliographic record

VenueRomanian Statistical Review Supplement · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Gross domestic productRecessionEuropean unionEconomicsContext (archaeology)Real gross domestic productInflation (cosmology)Eu countriesEconomyInternational economicsGeographyMonetary economicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

Romania recorded in the second quarter of 2014 a decline in gross domestic product (GDP) within the 28 European Union countries (EU) of 1% compared to the first quarter of 2014, and the only economies with negative developments were Germany, Italy and Cyprus. Estimates of GDP for the first quarter were revised down from the previous quarter growth of 0.2% to a decrease of 0.2%. Romania was in technical recession (two consecutive quarters of decline in GDP compared with the previous quarter) for two and half years 2008-2011 and back in technical recession in early 2012, and managed to return to growth by the end of that year. EUROSTAT data shows that the economies of Germany and Italy were contracted in the second quarter of this year by 0.2% and by 0.3% in Cyprus. In this context, the article proposes an analysis of the influence of the inflation rate and employment rate of labor force on GDP for EU countries and for Romania as an European Union member 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 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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.006
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.060
GPT teacher head0.380
Teacher spread0.320 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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