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Record W3112008899 · doi:10.51249/gei.v1i01.32

ASSESSING THE ACTIVITIES OF THE POLICE AS A PART OF THE NATIONAL SECURITY SECTOR:

2020· article· en· W3112008899 on OpenAlexaboutno aff
Олег Резник, Nadiia S. Andriichenko, Irina V. Zvozdetska, Volodymyr Zarosylo, Viktoriia I. Hryshko

Bibliographic record

VenueRevista Gênero e Interdisciplinaridade · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Studies and Reforms
Canadian institutionsnot available
Fundersnot available
KeywordsRelevance (law)LiabilityPerformance managementPublic relationsNational securityPolitical scienceBusinessComputer scienceLawMarketing

Abstract

fetched live from OpenAlex

The relevance of the article is conditioned by the need to ensure the effective activity of the subjects engaged in ensuring the national security, one of which is the police. Taking into account the fact that one of such tools is the assessment of police performance, the issue of studying the experience of the countries in this area is important to find the most optimal models for assessing the performance of these structures, which will contribute to their better fulfillment of the tasks assigned to them by law. The purpose of the article is to investigate the features of evaluating police performance, which will allow identifying advanced practices, to intensify the search for ways to improve the evaluation of police performance. The goal has been achieved using dogmatic, statistical, comparative legal methods, and systemic-structural approach. The authors revealed the specifics of the evaluation of police performance in Ukraine, the USA, Canada, France, and Great Britain. Emphasis is placed on evaluating police performance based on the level of trust of citizens and statistics. It is concluded that the trust of citizens in the police is necessary for the effective accomplishment of the tasks by the latter. It is noted that the use of statistical data to formulate an objective conclusion on the effectiveness of police performance is questionable because of the ability of the police management to manipulate the data. The areas for minimizing such manipulations are as follows: (1) introduction of administrative or disciplinary liability for manipulation of statistics on police performance; (2) conducting an independent review of the statistics provided by the police about their activities; (3) use of latent crime as a criterion for evaluating police performance. It is concluded that each of these areas needs considerable improvement. The findings complement previous research and have implications for improving the evaluation of the Ukrainian police and police of foreign countries.

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)0.010
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.033
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.364
Teacher spread0.319 · 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 designQualitative
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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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