GEOGRAPHY OF CRIMINAL OFFENSES OF THE CITY OF KYIV
Bibliographic record
Abstract
The article deals with the distribution of criminal offenses in the territory of Kyiv city in 2015-2018. The purpose of the article is to reveal the topic of crime in the city of Kyiv, as one of the most important problems of its further development as a European capital. The author focuses on the place of Kyiv in various international rankings, such as the rating of the international consulting company Mercer, the rating of the world’s largest database of cities and countries of the world, Numbeo et al. Using statistics from the State Statistics Service of Ukraine, the author compiled several tables: “The number of criminal offenses reported in the districts of the city of Kyiv in 2015-2018”, “Number of criminal offenses reported by the city of Kyiv by individual types in 2015-2018”, “Crime peculiarities of the city of Kyiv by regions in 2015-2018”, “Criminality of Kyiv City in different areas by regions in 2015-2018”, “Number of detected persons who committed criminal offenses in the city of Kyiv by districts in 2015-2018”. Based on the analysis of these tables, the rating of districts of the city of Kyiv for each of the studied years was drawn up, as well as the rating for four years together, the types of criminal offenses the number of which is the largest and the smallest in the city was selected. The author presents the probable reasons that lead to the predominance of theft, as well as grave and especially grave crimes, fraud and robbery over other types of crimes in the city. Using the rank method, the author identified the largest and least criminal districts of the city of Kyiv for each of the studied years. The article provides statistics on murders in capitals of different countries, including Kyiv, for 2012. 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 prone to commit crimes, and also offers measures to prevent the increase in the number of criminal offenses in the districts of Kyiv.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".