Construction of Fuzzy Information Assessment Method Based on Fuzzy Logistic Regression Algorithm in the Performance Evaluation of School-Enterprise Integration of Higher Vocational Hospitality Management Major
Bibliographic record
Abstract
School-enterprise integration is an effective way to improve the running level of higher vocational colleges and universities and stimulate the innovation vitality of enterprises.This paper takes the higher vocational hotel management profession as the research object, combines IPO model and intuitionistic fuzzy hierarchical analysis method (IFAHP) to realize the construction of schoolenterprise integration performance evaluation index system, and utilizes the fuzzy comprehensive evaluation method (FCE) to carry out specific application of this evaluation system.On this basis, the fuzzy set qualitative comparative analysis (fsQCA) was used to explore the specific path of schoolenterprise collaborative education in higher vocational colleges.The empirical study shows that the constructed evaluation system of school-enterprise integration has high reliability and operability, which is conducive to horizontal and vertical comparisons of higher vocational colleges and universities, and is also applicable to the authorities of higher vocational colleges and universities and the third-party evaluation organizations for the performance evaluation of school-enterprise integration.At the same time, it also indicates that the realization of high-performance schoolenterprise collaborative parenting programs in higher vocational hotel management majors cannot be achieved through a single variable, but rather through the form of conditional grouping to play a key role.There are five paths to improve the effectiveness of university-enterprise collaborative parenting in colleges and universities, and in the paths, the enterprise scale, the pre-project input and the project implementation process are the core conditions to improve the effectiveness of university-enterprise collaborative parenting in higher vocational colleges and universities, and the combination of changes of the three plays a decisive role in different condition grouping states.
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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".