Research on the Quality Evaluation System of First-Class Talents Training based on the Background of “Double First-Class” Construction
Bibliographic record
Abstract
The construction of “Double First-Class” is a Chinese strategy in line with the development trend of the world's higher education. In the “Double First-Class” construction plan, the state highlights the core position of talent training and plans to cultivate top-notch first-class talents. The cultivation of first-class talents is inseparable from the construction of talent cultivation quality evaluation system. Therefore, it is of great significance to design a reasonable first-class talent training quality evaluation system for improving the quality of first-class talent training and accelerating the development of “Double First-Class” strategy. Based on the background of “Double First-Class” construction and taking the graduate students of Dalian University as an example, this paper first collects data through questionnaire survey, and constructs a set of first-class graduate training quality evaluation system by factor analysis method. Then, based on the evaluation system, this paper evaluates the first-class graduate training quality of Dalian University by using fuzzy comprehensive evaluation method, so as to find out the shortcomings of graduate training in Dalian University. Finally, the paper puts forward the countermeasures and suggestions to improve the quality of first-class graduate training in Dalian University, in order to provide reference for other 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".