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Record W4392918836 · doi:10.47535/1991auoes32(1)020

CASCADING CONSEQUENCES OF UNEMPLOYMENT

2023· article· en· W4392918836 on OpenAlexaboutno aff
Florica Ştefănescu, Tünde-Ilona KELE

Bibliographic record

VenueThe Annals of the University of Oradea Economic Sciences · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicUnemployment and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsUnemploymentGross domestic productQuarter (Canadian coin)PovertyEconomicsOrder (exchange)Poverty rateDevelopment economicsMultitudePhenomenonOfficial statisticsDemographic economicsLabour economicsEconomic growthPolitical scienceGeography

Abstract

fetched live from OpenAlex

Combating poverty represents one of the challenges of the modern economy and Europe’s geopolitical situation contributes to worsening this problem. Economic growth and employment rate are determinants of the level of poverty in a society. Unemployment and its consequences are widely approached both in economic literature and in sociology and psychology. In the first part of the paper, we carried out a conceptual presentation of the economic and social consequences of unemployment at the national, individual, and family level. In the second part, starting from Okun’s law, we made an analysis of the relationship between the gross domestic product and unemployment rate for the period from 2005-2022 in Romania. In order to carry out the research, we used a series of statistical data regarding the unemployment rate available in the Monthly Bulletins of the National Bank of Romania (NBR) and data on GDP available on the website of the National Institute of Statistics of Romania (NIS). We worked with data regarding the situation in Romania from the first quarter of 2005 – fourth quarter of 2022. The conclusions of the paper converge towards the idea that unemployment is, nowadays, an increasingly complex phenomenon, being generated by a multitude of factors and which, in turn, determine multi-level direct and indirect consequences.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.189
GPT teacher head0.271
Teacher spread0.082 · 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 designTheoretical or conceptual
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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