Exploring Curricular Internships in Italy: Towards Entrepreneurial Universities
Bibliographic record
Abstract
This paper presents a database of the curricular internships offered by all Italian universities in different Courses of Studies (CoS), in the light of the challenge faced by university managers in shifting their institutions to a more entrepreneurial mode within a “triple helix approach” that highlights the relation between universities, government and enterprises. We built our database considering University Credits (UCs) attributed during the academic year 2014/15, consulting the websites of the Italian Ministry of Education and the official websites of 91 Italian universities. Although 3139 out of 4428 CoS (70.89%) offer curricular internships, these learning experiences in most scientific areas have a minor role in learning paths. These results also highlight the general sense of mistrust Italian universities place in the entrepreneurial world. The paper should enable university managers and policy makers to evaluate the activities carried out during curricular internships in Italy. The paper also provides useful insights to redefine the CoS offer in Italy. Data could be collected and updated yearly in order to monitor how the scenario is evolving. This paper contends that internships should be placed within the teaching mission in order to have an impact on entrepreneurship.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".