Research on the Incentive Mechanism of Organic Integration between Teaching and
Bibliographic record
Abstract
Abstract—Coordinate in undergraduate teaching and the scientific research in our country gained fruitful results, but the quality of higher education still exist many problems, including heavy research light teaching phenomenon is one of the most important problem. The organic integration of teaching and research incentive mechanism to promote teacher's teaching and scientific research, balanced and sustainable development, effective incentive mechanism is undergraduate research-based teaching implementation, teachers use, excitation, yukon, one of the important ways to leave, is to realize the condition on the undergraduate course colleges and universities teaching and scientific research management standardization.Heavy scientific research light teaching major disadvantages that exist in the phenomenon of undergraduate course teaching in our country.Based on the status quo that,in China, every college pay more attention to scientific research than teaching, learning advanced incentive mechanism for teachers in undergraduate education from abroad, I'll propose incentive mechanism on the construction of establishing favorable teaching bodies on both teaching management and practice, to improve teaching effectiveness, incentive system in the classroom, and teachers ' teaching enthusiasm and creativity. Promoting the coordinated development of teaching and scientific research. Keywords-teaching;scientific research;incentive mechanism; higher education;reform
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".