Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.037 |
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 it