Bibliographic record
Abstract
Abstract The book is a historical and comparative study of how and by whom the estates of deceased persons are administered, drawing upon the leading legal traditions of Europe and beyond. When a person dies, his or her assets (or their value) fall to be transferred to those entitled to succeed to them, whether in terms of the deceased’s will or, in the absence of a will, according to the rules of intestate succession. Along the way, the assets have to be identified, located, collected in, and safeguarded. Debts owed by the deceased or arising from the death must likewise be identified and then met (if need be, with the proceeds from a sale of estate assets). The whole process by which this is done, from the time of the death until the time of final distribution of the assets to those entitled to receive them, is the subject of the present volume. The focus is on the legal systems of Europe and of those countries which have been influenced by the European experience. So there are chapters on the law in Austria, England and Wales, France, Germany, Hungary, Italy, the Netherlands, Norway, Russia, Scotland, and Spain, as well as on Australia and New Zealand, Canada, China, South Africa, South America, and the United States of America. The historical background to the main legal traditions in Europe is represented by chapters on Roman law, on the customary law of early-modern Continental Europe, and on English law before 1837.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 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".