Research on the Educational Ecosystem of “Professional Entrepreneurship Integration” in Colleges and Universities
Bibliographic record
Abstract
This research solves the existing problems by scientifically examining the integration of professional education and entrepreneurship education in colleges and universities, based on the immersive innovation and entrepreneurship education purpose of “oriented to all, based on profession, strengthening practice, and running through the whole process of talent training”，based on the theory of educational ecosystem to build a university “professional entrepreneurship integration” educational ecosystem, based on the theory of the educational ecosystem to construct a “professional innovation integration” education ecosystem in colleges and universities, explore the thinking integration mode, practice integration mode, and faculty in the integration of specialization and innovation from the four aspects of specialized courses, specialized practice, specialized teachers, specialized platforms convergence mode, and platform convergence mode, exploring the organic integration model of professional education and entrepreneurship education. This research provides new ideas and new methods for the organic combination of professional education and entrepreneurship education in colleges and universities, it is a useful attempt to enrich and improve the development paradigm of “professional entrepreneurship integration”, and is of great significance to deepen the reform of higher education and the development of universities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".