Study on the Sharing Mechanism of Economics and Management Experimental Teaching Resources
Bibliographic record
Abstract
Experimental teaching is the key link for colleges and universities to cultivate innovative and entrepreneurial talents, and experimental teaching resources are the basis for ensuring the quality of experimental teaching. The uneven distribution of experimental teaching resources is a key problem faced by universities, and the resource-sharing mechanism is an effective means to solve this problem. Through the analysis of the current situation of the sharing of experimental teaching resources of economics and management majors in colleges and universities, the reasons for the lack of resource sharing are summarized. Combined with the main problems in the sharing of experimental resources, this conceptual paper proposes to strengthen the leading role of the government in the construction of the platform for sharing experimental resources, improve the promotion and incentive mechanism of experimental teachers, improve the management mechanism of experimental teaching, enhance the comprehensive service ability of the teachers of the experimental platform for sharing software and hardware resources, and expand the scope of mutual recognition of credits among universities, strengthen the exchange of laboratory talents and achievements to strengthen the construction of the sharing system of economic and management experimental teaching resources, so as to realize the complementary advantages of experimental teaching resources among colleges and universities, improve the utilization efficiency of experimental teaching resources, and jointly improve the quality of personnel training.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".