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Record W3208096936 · doi:10.5539/ies.v14n11p31

Developing Student Work Experience Programmes Within the European Higher Education Area Framework: The Role of Social Partners

2021· article· en· W3208096936 on OpenAlexvenueno aff
Dimitrios Skiadas, Sofia Boutsiouki, Vasileios Koniaris, Konstantinos Zafiropoulos, Marianthi Karatsiori

Bibliographic record

VenueInternational Education Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
FundersHellenic Foundation for Research and Innovation
KeywordsEmployabilityHigher educationWork (physics)Bologna ProcessSocial partnersPublic relationsSocial changePedagogySociologyPolitical scienceEconomic growthEconomicsEngineering

Abstract

fetched live from OpenAlex

The aim of establishing the European Higher Education Area (EHEA) marked the development of the Bologna process since its beginning, while it exercised a decisive influence on the content of the higher education policy initiatives undertaken over the years. One of the most important goals of the relevant policy making was to bridge the university-to-labour market gap and to improve the employability of graduates. Such aims require a consistent and multidimensional cooperation between higher education institutions and the social partners, mainly employers, from which significant benefits may derive for all parties involved. As a result, many types of work based learning have been promoted in higher education with the most prominent of them being the student work experience programmes organised by universities in collaboration with enterprises. The paper analyses the guidelines provided by the EHEA framework with regard to the cooperation between universities and the social partners. Also, it discusses the role that has been attributed to (or claimed by) the social partners regarding work experience programmes. The EHEA institutional framework includes provisions for the participation of social partners in the organisation of work placements, which contribute to students’ skills development and easier transition to employment.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.682

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.123
GPT teacher head0.488
Teacher spread0.365 · 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 designQualitative
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

Citations1
Published2021
Admission routes1
Has abstractyes

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