Using Internships to Enhance Engineering Programs – the Case Study of an Industrial Partner
Bibliographic record
Abstract
Many institutions recognise the importance of internships.As part of the training of future engineers, theseopportunities confront students to the realities of jobmarket. The University studied in this case is one of thoseinstitutions. It has developed its way of integratinginternships with the engineering bachelors degree.The University in question gives high importance topractical knowledge. As third and fourth year studentscomplete their studies, they are asked to manage full-scaleprojects – from Design Brief to assembly, along withPlans and Specifications. These courses are bound to theUniversity's engineering Chair, who accompaniesstudents in their design process. All subjects come fromreal customers who either have heard of the Chair, ordiscovered it via internships. In fact, a major part of theChair's clients comes from partnerships developed insummer time, outside of the walls of the University: theconfidence and trust built in internships propel theprogram. Recently, this symbiosis was brought up to anew level
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".