THE NATURE OF INNOVATION ECO-SYSTEM OF THE WESTERN KAZAKH STATE UNIVERSITY
Bibliographic record
Abstract
It was a strong belief that higher education institutions are notoriously resistant to change. However, during the COVID-19 pandemic, universities have quickly and effectively moved millions of students and educators online despite huge logistical and technological challenges. There are very few industries that have reacted in this way. In future leading universities will look for a new business model and apply disruptive innovations into the leaning process.Today is a right time for planning a long term innovation strategy. In recent years Kazakh higher education development has been accompanied by intensive economic growth and raising demand for high qualifies employers. The aim of this research is to reveal the ways of implementing high innovation and creativity approach in universities under example of Western Kazakhstan State University. This study examines the factors determining conditions for development of innovation culture across the university and industry. The methodology is based on expert interviews, reflective experiences; surveying research for innovation, incorporating the information on innovation landscape map, university infrastructure, human resources, PESTEL analysis as well as industry overview. The results show that WKSU needs frugal innovation, as it provides a new entrepreneurial landscape for companies in low-income countries with limited resources to develop innovations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".