Building bridges between university and industry: theory and practice
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the level of benefit to learning through developing strong links between universities and industry, and to suggest a methodology for building bridges between university and industry that provides a full learning experience for students. Design/methodology/approach A case study approach which included the development of interactive projects that join students with industry, and follow‐up questionnaire surveys of the outcomes, carried out among students and businesses. Findings It was found that both parties feel that they benefit from building bridges between universities and industry, and data from this research are reported on in greater detail in the latter part of this article Research limitations/implications Research is limited to students following the Manufacturing Management and Quality Systems courses within the Industrial Engineering Department of the Tecnologico de Monterrey, Mexico, over a one year period. Practical implications Provides evidence for a positive factor that linking university students and industry in joint projects increases the potential for a fuller learning experience for the students. Originality/value The paper is based on actual experience of students, teachers and companies who participated in this experimental learning process.
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 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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.015 | 0.014 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".