Study on the Reasoning Traceability and Reform of Family Related Judgment Documents
Bibliographic record
Abstract
How to present good reasons in judgment documents has been a common problem. This study takes family judgment documents as the pointcuts and is going to discuss how to achieve effective reform through the reasoning traceability analysis. In current stage, China has not promulgated separate legal regulations on family related litigation, the corresponding reasoning part is based on the relevant provisions of civil proceedings, such as the guidance related to the writing of civil judgment document issued by the Supreme People's Court in 2016. Although this document makes some provisions on reasoning in civil judgment document, taking the consideration of certain specialties of family cases, the identities of the parties also have special certain natures, reasoning in family judgment documents usually focus more on the integration of "emotion". Therefore, to standardize family judgment documents at this stage only by relevant provisions of conventional civil litigation is not sufficient, and not able to meet the demands of family cases, and it also shows problems in judicial practice. In 2016, China comprehensively carried out the pilot reform of family justice, which plays an imperative role in following research and development of family judgment document. And the level of reasoning in family judgment document improved in the process of form innovation and practice experience accumulation. However, the pilot work does not set up standardized and complete specifications on making family judgment document. Through the combination on the theoretical analysis and judicial practice cases, and the summary of main problems encountered at present, this research will propose suggested countermeasures, and is targeted to facilitate the development of reasoning in family judgment document.
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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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".