GEOGRAPHY OF CRIMINALITY OF UKRAINE
Bibliographic record
Abstract
The article deals with the spreading of criminal offenses in Ukraine in 2017. The purpose of the article is to reveal the crime topic in Ukraine as one of the most important problems of its further development as a European state. The author focuses on the place of Ukraine in the international ratings, such as the Global Index of the World, the Global Index to terrorism etc. Using statistical data from the State Statistics Service of Ukraine, the author has compiled a table of the level of criminal offenses by regions of Ukraine. Basing on the analysis of the table, the areas in which the crime in 2017 grew or decreased (in compressing with the previous year) is highlighted. The rating of areas for 2017 and 2016 was compiled and compared with each other. The article highlights the types of criminal offenses the number of which are the largest and the smallest in each area of Ukraine. The author presents the probable reasons that lead to the predominance of thefts, as well as grave and especially grave crimes over other types of crimes in the regions of Ukraine. Attention is paid to a criminal offense related to pimping. The areas in which in 2017 were recorded the cases of pimping are listed. The author of the article counted the number of crimes in the regions of Ukraine per 1000 people. Highlighted areas with the highest, average and lowest number of crimes per 1000 inhabitants. The author emphasizes that educated people leave the country for Europe, Canada, the United States, China and other countries, reducing the number of intellectuals who are less inclined to commit crimes. The article describes the main factors that determine the geographical differences of crime and the measures to prevent the increase of the number of criminal offenses in the regions of Ukraine is proposed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".