Research on the Intermingling of Intelligent Computing and Traditional Culture to Promote Innovative Talent Cultivation Mode under the Perspective of New Quality Productivity
Bibliographic record
Abstract
Taking the perspective of new quality productivity, this study explores the promotion effect of the intermingling of intelligent computing and traditional culture on the cultivation of innovative talents, and constructs an evaluation system containing four primary indicators and 14 secondary indicators of educational activities, student practice, collaborative innovation and teaching resources.The cloud integration model is used to deal with the ambiguity and randomness of the complex system, and the network hierarchy analysis method ANP is used to determine the weights of the indicators and reveal the dynamic association of each element.It is found that: the indicator B2 of student practice category has the highest weight of 0.329, in which the number of awards of C5 innovation competition and the number of C4 students' project participation are the core driving factors, with the weights of 0.103 and 0.078, respectively.the cloud integration model verifies the scientificity of the evaluation system.The evaluation value of the traditional culture innovation talent evaluation system constructed in this paper is 0.798, and the integrated cloud model belongs to "very good" grade.However, the mapping intervals of C14 Resource Library Call Frequency and C13 Teacher Integration Background are low, 0.346 and 0.413 respectively, which need to be adjusted and optimized.The innovative talent cultivation program of colleges and universities constructed in this study can make up for the shortcomings in traditional talent cultivation performance evaluation, has certain practicality and effectiveness, and helps to improve the quality of traditional culture innovative talent cultivation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".