Precincts of Police Officers in 1878: the Experience of Quantitative Research (on the Example of Kazan and Perm Provinces)
Bibliographic record
Abstract
The object of the study is the institute of police officers, introduced in the Russian Empire in the last quarter of the XIX century. The subject of the study is the district land plots created in the framework of the reform in the Kazan and Perm provinces. The purpose of the study is to analyze their quantitative characteristics. The theory of modernization is chosen as a general methodology. To achieve this goal, quantitative methods are used, first of all: formal quantitative, correlation and multidimensional (cluster) data analysis. The basis for the quantitative analysis was the "Information" on the distribution of provinces into the district plots (1878), deposited in the Russian State Historical Archive. The main conclusions of the study are the idea that the situation of the police constables of the Perm province was much worse than in Kazan, and the work of the constables was hindered by a significant number of the population of 1/3 of the police stations. The novelty of the study lies in the fact that for the first time in Russian historiography, correlation and multidimensional cluster analysis by the k-means method was used to analyze the uryadnik sites of the Russian Empire. As a result, for the first time, the classification of district plots was created. 1) sparsely populated; 2) Kazan-type; 3) scattered; 4) overpopulated; 5) too large; 6) scattered and overpopulated district plots were identified.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".