About the features of rural settlements that influenced the variability of names and the tools for their identification (on the example of the rural of the Ruza district in the middle of the XVI—first quarter of the XVII centuries)
Bibliographic record
Abstract
This article presents the results of the analysis of the opportunities that open up when using the Big Data tool to identify the names of settlements and landholdings that tended to change over time in the documents of the statistical accounting of the Russian state of the second half of the XVI—first quarter of the XVII centuries. The procedure for analysing the features of information about objects available in the documents of the specified epoch is described using which it becomes possible to judge the predisposition of their names to variability. The research was performed on the basis of information about 22 objects located on the territory that was formed by the beginning of the XX century in the Ruza district of the Moscow province. Statistical analysis tools were used to identify the dependencies that took place. The result of the research presented in this article is the proof of the possibility and effectiveness of using the Big Data tool to establish the correspondence of information about the same real estate objects mentioned in the materials of different eras under different names. The research shows that the names of objects formed considering the characteristics of their owners were subject to the greatest variability in the middle of the XVI—first quarter of the XVII centuries. The actual information about the objects obtained during the research can be used to compile cartographic materials reflecting the history of the formation and development of the economy of the territory of the Ruza district as well as when performing work by specialists conducting research in the fields of history, sociology, geography, surveying and other related fields of knowledge.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".