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Record W2101694733 · doi:10.1177/0020715207088586

Transition from Educational System to Labour Market in the European Union

2008· article· en· W2101694733 on OpenAlexvenueno aff
Ellu Saar, Marge Unt, Irena Kogan

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Policy and Reform Studies
Canadian institutionsnot available
FundersGlobal Development NetworkEesti Teadusfondi
KeywordsLabour market flexibilityEuropean unionFlexibility (engineering)EconomicsWelfareLabour economicsMember statesMarket economyInternational economicsEconomic growthUnemployment

Abstract

fetched live from OpenAlex

Theoretically, the central research question of this article pertains to the way in which national institutional arrangements, namely educational systems, and related modes of labour markets and welfare provisions, affect the aggregate effectiveness of youth labour market integration in the new EU member states in comparison to the old EU countries. The study utilizes the European Union Labour Force Survey 2004. Results of the cluster analysis provide substantial support for distinct patterns of labour market entry in terms of the stratification of labour market exclusion, downgrading risk and labour market mobility of LM entrants in different CEE countries. Furthermore, the article reveals also new aspects of labour market entry in the EU-15 countries while considering not only educational signalling but also the labour market flexibility dimension.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.062
GPT teacher head0.391
Teacher spread0.329 · 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

Citations90
Published2008
Admission routes1
Has abstractyes

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