Foreign experience of evaluating the effectiveness of police activity and the possibility of its implementation in Ukraine
Bibliographic record
Abstract
The article is devoted to the study of foreign experience in assessing the effectiveness of police activities and the analysis of the possibility of its application in Ukraine. In particular, the author examines the foreign experience of evaluating the effectiveness of police activities in such countries as Austria, Great Britain, Estonia, Canada, Latvia, Lithuania, the USA, France, and the possibility of its implementation in Ukraine. Particular attention is paid to evaluation methods that combine quantitative and qualitative indicators of police performance. The authors point out the importance of internal and external control, with a significant role for public control, which is ensured through surveys, reporting and other forms of interaction with the public. The experience of foreign countries also includes the use of statistical reports on crime, such as in the United States and Austria, which allow not only to assess the work of the police but also to increase the level of trust in law enforcement agencies. It is concluded that the introduction of a point-based performance evaluation system should be applied to the personal assessment of each police officer. Based on the study, we conclude that in all foreign countries analyzed by us, today much attention is paid to the assessment of the effectiveness of police activities, which is due to the social purpose of its activities, but, given the existing legal system, organizational and legal framework for the activities of police units, and the adopted system of their management, the criteria for such assessment differ. In addition, we have found that certain forms of police performance evaluation, such as public opinion polls and annual reports on police performance, are common to both national and most foreign police systems. As a result of summarizing foreign experience in assessing the effectiveness of police activities, the author suggests using the following positive issues, namely: introduction of the above-mentioned US experience in Ukraine, since such ‘Crime Maps’ are available; use of the positive experience of the HM Inspectorate for Constabulary (UK) in the activities of the newly established Department of the Main Inspectorate and Human Rights of the National Police of Ukraine, in particular, in assessing the quality of police services.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".