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Record W3123849349 · doi:10.24193/ed21.2020.19.08

Correlation between school dropout and gross domestic product in the emerging countries of Central and Eastern Europe

2020· article· en· W3123849349 on OpenAlexaboutno aff
Ioana-Maria Vodă

Bibliographic record

VenueEducatia 21 · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsnot available
Fundersnot available
KeywordsCzechSchool dropoutDemographic economicsGross domestic productDropout (neural networks)Quarter (Canadian coin)Positive correlationGeographyProduct (mathematics)DemographyCorrelationEconomic growthDevelopment economicsPolitical scienceEconomicsSociologyMedicineMathematics

Abstract

fetched live from OpenAlex

By school dropout is meant leaving the educational system, regardless of the reached level, before obtaining a qualification or a complete proffesional training or before the end of the study cycle started (Doron & Parot, 2006). Although there are numerous researches on school dropout, we consider that it is necessary to study the relationship it has with the level of development of a country, in order to identify new causes and possible solutions to this phenomenon. In this study we included seven emerging countries of Central and Eastern Europe: Bulgaria, Czech Republic, Poland, Romania, Slovakia, Slovenia and Hungary. Thus, we analyzed the correlation between the annual gross domestic product and the school dropout rates in the mentioned countries. The data were extracted from the European Commission’s Eurostat database and entered into the SPSS and then analyzed using the Forward prospective procedure. This research revealed the existence of significant negative correlation between the level of development of a country and the school dropout rate in the countries included in the analysis, in female, but also insignificant or weakly significant correlations between the mentioned variables in males.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.286
Teacher spread0.271 · 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 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
Published2020
Admission routes1
Has abstractyes

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