Developing Student Work Experience Programmes Within the European Higher Education Area Framework: The Role of Social Partners
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".