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ПРОБЛЕМЫ ПРОТИВОДЕЙСТВИЯ ХИЩЕНИЯМ В ПОСЛЕДНЕЙ ЧЕТВЕРТИ XIX - НАЧАЛЕ XX ВЕКОВ (НА ПРИМЕРЕ ОБЩЕЙ ПОЛИЦИИ УРАЛА)

2025· article· ru· W4415695608 on OpenAlexaboutno aff
Сергей Михайлович Рязанов

Bibliographic record

VenueBulletin of Udmurt University Series History and Philology · 2025
Typearticle
Languageru
FieldSocial Sciences
TopicSocial and Behavioral Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic shortageQuarter (Canadian coin)Period (music)

Abstract

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Научная новизна исследования состоит в том, что впервые в отечественной историографии тема противодействия хищениям в 1878-1904 гг. рассматривается на общеуральском материале. В исследовании анализируются и количественные данные о колоссальном росте имущественных преступлений в рассматриваемый период, и практики работы полиции. Для раскрытия преступлений полиция применяла и традиционные расспросы местного населения, и внутреннюю агентуру из преступной среды. Для предупреждения, как правило, использовалось полицейское патрулирование. В результате исследования сделан вывод о том, что полиция Урала недостаточно эффективно раскрывала и, в особенности, предупреждала хищения по причине недостатка кадров, их низкой квалификации и перегруженности другими обязанностями. Особую роль играло отсутствие конных полицейских на большей части территории Урала, которые могли бы предупреждать конокрадства и грабежи, совершаемые верхом. The purpose of the article is to analyze the activities of the Ural police (the last quarter of the 19th - early 20th century) in combating theft and to identify problems associated with the practical implementation of this function. The author used a significant range of office documents from the funds of four archives of the subjects of the federation and two central archival institutions. Materials from the local periodical press played an important role in understanding the policing of police officers in solving thefts. Historical-genetic, historical-systemic and formal-quantitative methods were mainly used to analyze sources and literature. As a result of the study, it was concluded that the Ural police were not effective enough in solving and, in particular, preventing thefts due to a shortage of personnel, their low qualifications and overload with other duties. A special role was played by the absence of mounted policemen in most of the Urals, who could prevent horse thefts and robberies committed on horseback.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0030.005
Scholarly communication0.0090.004
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0390.010

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.

Opus teacher head0.021
GPT teacher head0.227
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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