Employment of Geoinformation Technologies in Historical Researches Experience of Kazan (Volga Region) Federal University
Bibliographic record
Abstract
Relevance of the topic is due to the prevailing in modern science trend - complex study of society, not only in time but also in space, established thanks to the convergence and integration of Arts and Sciences. The paper aims to describe our own experience of association within the framework of a single information field of full-text data sources, mainly registration, accounting and statistics data (cadastres, scribe and census books) and maps to represent in terms of spatial coordinates the social stratification of the city Sviyazhsk and its surroundings in the XVII century. The leading method in the research of this problem is that of geo-information technologies. The main results of this work were the establishment of historical sources database of the period under study, data formalization of the written sources, compiling the geographic basis of geo-information system (GIS), digitization and creation of vectorized base of geo-data (BGD), which allows to localize and identify the settlement of Sviyazhsk County. The paper materials will be useful for further research and for model development of a settlement structure, farm tenure and land use, social stratification of the population and multifaceted relationships of towns and villages (peasantry) in the Middle Volga area in the second half of the XVII century.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".