Change of the Higher Education Paradigm in the Context of Digital Transformation: from Resource Management to Access Control
Bibliographic record
Abstract
Digitalization and transition to a new technological structure bring humanity to another level of development. The changing technological structures, industry and society progress, enhance the importance of improving the university development model. The existing management system and infrastructure in universities are often outdated and unable to ensure their competitive and adequate functioning. Hence, the need to improve the processes of using the university infrastructure through digital technology. The composition and range of the resources should also be reviewed and supplemented with new components.The purpose of this work is to reveal the principles and requirements for improving the university infrastructure using digital technology.The methodology is based on modeling the university management system, with the concept of infrastructure logic as a core, meant to include new elements in the university management infrastructure: university stakeholders, cultural values, investments and translation.The management model transformation implies a transition from structural to infrastructural approach, from infrastructure management to managing the infrastructure logic. The digital network platform incorporating the information on all the infrastructure facilities, their status, will provide effective user access management to each university resource.The recommendations formulated to improve the university infrastructure using digital technology will make higher education more effective.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".