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Solution of the Personnel Issue in the City Police of the Russian Empire at the End of the 18th – the First Qarter of the 19th Centuries: Normative and Legal Regulation

2022· article· en· W4313361579 on OpenAlexaboutno aff
Sergey Chikov

Bibliographic record

VenueIzvestia of Smolensk State University · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLegal and Regulatory Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEmpireQuarter (Canadian coin)LegislationLawOfficerNormativeStaffingHistoriographyOrder (exchange)Promotion (chess)JurisdictionPolitical sciencePublic administrationSociologyHistoryPoliticsBusiness

Abstract

fetched live from OpenAlex

The article analyzes the evolution of the legislation of the Russian Empire in the field of personnel policy in the city police of the declared period. When considering the degree of the problem under study, special attention is paid to modern historiography. The source base of the study is represented mainly by the legal acts of the Russian Empire at the end of the 18th – the quarter third of the 19th centuries. The analysis of legal documents allowed the author to conclude that the dynamics of personnel policy in the field of work of city police in the late 18th – first quarter of the 19th centuries were aimed at strengthening public order and security. At the same time, the maintenance of the city police was shifted to the local authorities. A special role in the appointment of officer ranks in the city police was played by the Committee of August 18, 1814. It is noted that the following circumstances had an inhibitory effect on the implementation of an effective personnel policy in the field of public order. Firstly, the class structure of society limited the opportunities for recruiting and promotion of people from the unprivileged classes, which was also reflected in the different approaches to working with officers and lower ranks. Secondly, the solution to the problems of the city police was seen in increasing its staffing, and not in strengthening its material base, raising salaries, improving training and retraining of personnel.

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)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.006
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.008
GPT teacher head0.202
Teacher spread0.194 · 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
GenreOther

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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