К вопросу о реконструкции комплекса жалованных грамот XVII В. Большому Тихвинскому Успенскому монастырю
Bibliographic record
Abstract
Based on the study of the monastery`s letters patent and inventories of property, the article aims to restore the letters patent of the 17th century for landed property of the Tikhvin Uspensky monastery in Novgorod uyezd. Restoring letters patent helps to study the archive of the monastery because record keeping of landed property determined the structure of the archive. These legal documents may be divided into three groups: 1) letters patent of the second half of XVI — the first quarter of the XVII century, incorporating information about landed property acquired in 1613/14 years; 2) letters patent for the land in Nikolaevsk Shungsky and other church yards; 3) letters patent for fishing. Analysis of the materials showed that a significant number of the letters patent of the XV — the first quarter of the XVII century failed to be preserved. The letters patent were not recorded in the inventories of property and their texts are not presented in monastery registers. Based on the data from the preserved documents, we can trace the history of individual land grants to the monastery and their recorded evidences up to the end of the 17th century.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".