Egyes rendi jogintézmények hatása a nemesi családnévviselésre = The influence of some feudal legal institutionson the surnames of the Hungarian nobility
Bibliographic record
Abstract
In Hungary, in the period of the stabilization of two-constituent names, the nobility, setting an example also in name bearing, accepted several regulations resulted in name changes, each of which was rooted in the legal system of the feudal Hungary. Marriage (as a result of its property right consequences), changes in estates, assignment in kind of the quarter inherited by a daughter, investment with the rights of a son, contemporary forms of adoption could all lead to (deliberate) changes in surnames, as these feudal legal institutions influenced financial conditions. This paper explains the so far neglected aspects of the relation between the history of law and name bearing, and describes the contemporary plasticity of name use as observed in the cases of some noble families.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".