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Record W2280416747 · doi:10.1177/0020715215587772

Work experience during higher education and post-graduation occupational outcomes: A comparative study on four European countries

2015· article· en· W2280416747 on OpenAlexvenueno aff
Giampiero Passaretta, Moris Triventi

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
Fundersnot available
KeywordsGraduation (instrument)EmployabilityUnemploymentHigher educationWork experienceTypologyVocational educationWork (physics)Demographic economicsEmpirical researchFlexibility (engineering)Labour economicsPsychologyPolitical scienceSociologyEconomicsEconomic growthPedagogyManagement

Abstract

fetched live from OpenAlex

This article examines the relationship between work experience acquired during higher education and post-graduation labour market outcomes in four European countries: Germany, Italy, Norway and Spain. A theoretical framework that shows in which institutional contexts work experience may be a ‘competitive advantage’ for young graduates is developed. In the empirical analysis, data from the Higher Education and Graduate Employment in Europe (CHEERS) and Research into Employment and Professional Flexibility (REFLEX) surveys are used to examine the effect of a typology of student employment (accounting for both length and coherence of work experience with the field of study attended) on several occupational outcomes 4–5 years after graduation. The empirical results show that, in Italy and especially in Spain, work activities during tertiary education are associated with better labour market positions after graduation: any type of work experience increases employability and reduces the risk of unemployment, and furthermore, previous work experience – especially when coherent with the field of study – decreases the probability of skill mismatch in future occupations. The effect of student employment, however, is smaller for most outcomes in Germany and negligible in Norway.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.574

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 studies0.0000.000
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 head0.154
GPT teacher head0.378
Teacher spread0.224 · 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

Citations55
Published2015
Admission routes1
Has abstractyes

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