Flaws and Improvement of the Legal System for Forfeiture of Inheritance
Bibliographic record
Abstract
The legal system of forfeiting the right of inheritance adheres to “the principle that no one shall profit from his illegal behavior”, based on the Civil Law theory. The forfeiture of the right of inheritance consists of absolute forfeiture and relative forfeiture. In China, although the Inheritance Law made the provisions of absolute forfeiture of the right, they are excessively rough and recapitulation. Meanwhile, the provision about relative forfeiture of the right was unduly narrow. Falling into the category of the private law, the legal system of inheritance law should be based on interested parties’ intention as well as the public adjudicators’ general standard. In order to maintain and stabilize the normal inheritance procedure and protect the legitimate rights and interests of those law-abiding parties, the future improvements of some major documents concerning the Inheritance Law is hereby advised to modify as, in terms of the restoration of the right of inheritance after its forfeiture, “confirmation from judicial process is after the application made by the decedents whose forgiveness shall be asked for.”
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 | 0.038 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".