The Reform of Undergraduate Teaching on the “History of Foreign Law”
Bibliographic record
Abstract
For a long time, in the "History of Foreign Law" teaching, there are some common problems as following problems: lagging teaching content, outdated teaching methods and means.In order to solve the above problems, Xiangtan University carried out fruitful reform and innovation of the teaching content, teaching methods and teaching methods in "History of Foreign Law" teaching. Keywords: Foreign legal history, Content of teaching, Methods of teaching, Means of teachingWe have already entered the era of knowledge economy.At present, personnel training goal of China is to foster innovative talents.Teaching content directly reflects the purpose of teaching instruction and personnel training goal which is a solid support to achieve innovative personnel training pattern and a core element to improve the quality of education .In order to meet the development needs of knowledge-based economy, we must break the traditional knowledge structure, and strive to innovate the teaching content, and internalize it into abilities and qualities with independent thinking, self-exploration, creative thinking ability of anyone who receives an education through innovative teaching methods and means, insist connotation and extension n of the unity of teaching content update strategy.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".