Bibliographic record
Abstract
Testaments are unique resource in the search for an understanding of the everyday lives of the residents of medieval towns. Through this medium it is possible to investigate religiosity, to analyse the social stratification of the population, and to map property relationships, family life or the relationship of individuals to death. In this country they have survived in a number of towns, including Český Brod, Plzeň (Pilsen), Kutná Hora, Litomyšl, Soběslav and Kadaň. The greatest number of testaments are found in Prague, where collections survive in the municipal rolls, along with inventories of effects in later periods, for almost the entire period from the 15th through to the 19th centuries. The oldest Czech testaments are in the memorial books of the Prague Old Town (MS 986, Liber vetustissimus) and date to the period beginning 1360 and ending 1419. A collection of some 80 testaments from the years 1428-1441 is to be found in a memorial tome (MS 992) of the Prague Old Town, while testaments from the Lesser Town (Malá Strana) survive in the council manual. An ever more designated agenda required the separation of books intended for the administration of effects from memorial or markets books. The New Town's Prague testaments are the most complete collection of Late Medieval testaments found within the...
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".