Optimization Modeling Research on Multi-Objective Resource Allocation for AI-Enabled Innovation and Entrepreneurship Education
Bibliographic record
Abstract
Innovation and entrepreneurship, as an important part of social and economic activities, has received more and more widespread attention.Based on the characteristics of the digital era, the study uses artificial intelligence to empower innovation and entrepreneurship education in colleges and universities.Optimize the allocation of innovation and entrepreneurship education resources in colleges and universities through multi-objective optimization algorithm.Construct an optimization model of resource allocation for innovation and entrepreneurship education in colleges and universities, and verify its resource optimization and allocation performance.Taking 13 colleges and universities in C city as the research object, the optimization of their innovation and entrepreneurship education resource allocation is processed.The MSS cumulative values of this paper's multi-objective optimization model on the CPLX problem and the MATP problem are -1.400 and -1.033, respectively, which are the smallest among all models, with the best performance and ranked the first in resource allocation efficiency.After optimization, the resource allocation level of innovation and entrepreneurship education in all 13 colleges and universities has been improved, and the resource allocation among the colleges and universities is more balanced.The resource utilization efficiency of innovation and entrepreneurship education in the 13 colleges and universities has been improved by 17.02% on average.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".