Bibliographic record
Abstract
For a long time, dispute resolution and alternative techniques like mediation have been dealing with a classic conception: every part involved in dispute resolution was carrying exactly one patrimony. Irrespective of physical or moral person the rule was the same: one person, one patrimony. Alternative dispute resolution, like mediation, dealt with persons in order to reach a mutual agreement affecting their unique patrimony. The rule is already history. Still remain the first premise: every person has a patrimony. But under present Civil code the provision is stopping here. As a result, the uniqueness of the patrimony vanished from new law. Dealing with different patrimonies a dispute solver should be able to understand the new notion and to assist the parties to finals agreements according to the rules of the divisions of the patrimony. First at all we should observe that any division of the patrimony of a person have to have a legal basis. The “liberalisation” of the patrimony is not so advanced in order to accept any voluntary division of the patrimony of the person. Second, the prominent creation in this field are represented by fiducia (a kind of Anglo-Saxon trust concept) and assigned patrimony. Fiducia is new for our legal system only, following in fact the Quebec civil code regulation. The assigned patrimony was already been present in our legislation. The Ordinance no 44/2008 was dealing with this concept in commercial field.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".