Bibliographic record
Abstract
The cooperation between universities and industries is already one of the most important factors driving the national economy in the knowledge-based society of the 21st century represented by the Fourth Industrial Revolution. The Korean government has also been carrying out legal and institutional re-adjustments to promote industrial-university cooperation in line with demands for such changes in the times. However, despite this industry-academic cooperation system, there is still a significant mismatch between industrial demand and the university's workforce development system. By the way, there is a Cooperative Education(CO-OP) in Canada and the United States. It’s an innovative link between the university and the industry. The reason is that the CO-OP program not only allows students to gain experience with their majors in the industrial field, but also plays a positive role in improving their specialty expertise. In particular, field information, ideas, and job insights that students acquire through CO-OP also serve as motivation for starting a business beyond employment after graduation. Furthermore, CO-OP experience is an important opportunity for future researchers to come up with commercialized research results that are not separated from the field sites The purpose of this study is to overcome the gap between industrial demand and the college manpower training system, and develop a Korean-style coaching program model as a growth engine for creative talent-building policies, represented by 'creation of start-ups and new industry.' In addition, this study suggested measures that can be applied in real universities. In addition, the study also highlighted that the introduction of CO-OP programs with field practices in Korea could also boost start-ups. Based on the Korean CO-OP program model, the curricula applicable to domestic universities consisted of two types : general and research-oriented university types.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.013 |
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".