Contextualizing Relations Between Presumptions and Legal Fictions: An Analysis of the Chinese Civil Code
Bibliographic record
Abstract
This research aims to achieve two goals: first and foremost, clarify the similarities and dissimilarities among statutory (legal) presumptions, judicial (factual) presumptions and legal fictions. The second is to provide a set of theoretical tools that can correctly distinguish two types of presumptions from legal fictions, so as to facilitate the accurate identification and application of those three by Chinese judges in their judicial practice. This study mainly adopts two research methods: legal theory analysis and law article analysis. The research results of this paper mainly are: first, compared with legal fictions, two types of statutory presumptions are more or less refutable. Their differences are as follows: on the one hand, the scope of refutation is different; on the other hand, the difficulty of refutation is different, too. Second, litigators are forbidden to refute the conclusive part of an applied legal fiction, but they can disprove its premise fact. By nature, that refutation is “a challenge against the lawfulness of that legal fiction’s usage”. Third, for related ultimate facts, the using of statutory presumptions will not lead to their reversed burden of persuasion. Fourth, when the principle of presumptive fault is applied, as for the issue of whether the defendant has subjective fault or not, the related burden of persuasion will be reversed to be assumed by the defendant. By comparison, in the usage of statutory presumptions, there will be no inversion of burden of persuasion. Fifth, direct denials, indirect denials and defenses can be used to rebut premise facts of the legal fiction, basic facts of two types of statutory presumptions, and presumptive facts of the refutable statutory presumptions. Sixth, when direct denials and indirect denials are launched, the evidence is the disproving evidence (Gegenbeweis). When the defenses are raised, the evidence is the proving evidence (Hauptbeweis). Seventh, the successful effect of discrediting basic facts of refutable statutory presumption: the using of that presumption lacks legitimacy, so the corresponding presumptive facts are untenable, too. Eighth, the successful effect of contradicting the presumptive facts of the refutable statutory presumption: while those presumptive facts are proved groundless, the related basic facts will be considered as confirmed continually. And finally, the successful effect of disproving basic facts of irrefutable statutory presumption: because it has been proved that the using of that presumption is lack of lawfulness, the related presumptive facts can not be sustained, either. By making use of those aforesaid study results, 28 statutory presumptions and 30 legal fictions are identified in the Civil Code of China.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".