The Dilemma of Vice Chancellors of Law and Its Improvement—Based on the Analysis of Court Personnel
Bibliographic record
Abstract
Nowadays, the state attaches more and more importance to the protection of minors and legal education, and with the implementation of the Measures for the Appointment and Management of Vice Chancellors of Law in Primary and Secondary Schools, the coverage of vice chancellors of law in primary and secondary schools has become more and more extensive. However, in practice, there are a series of problems such as the lack of enthusiasm of court personnel as vice chancellors of law, the lack of full implementation of responsibilities, the unbalanced provision of resources and the too single teaching mode, which make it difficult to guarantee the efficiency and effectiveness of court personnel as vice chancellors of law. Therefore, the functions of judge and vice chancellors of law should be organically combined, and the system of employment, management and assessment of vice chancellors of law, as well as the content and form of innovative legal education should be continuously improved to improve the efficiency of vice chancellors of law, so that "vice chancellors of law" has a "name" and "reality" establishment of legal thinking of minors should be promoted to take care of the healthy growth of minors comprehensively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".