Bibliographic record
Abstract
The article examines the phenomenon of domestic violence against men in the context of the modern Ukrainian and international legal system. The authors draw attention to the fact that despite the formal gender neutrality of Ukrainian legislation, in particular Article 126-1 of the Criminal Code of Ukraine and the Law of Ukraine “On Prevention and Combating Domestic Violence”, male victims remain virtually invisible in law enforcement practice. An analysis of court decisions, statistical data, research results and the work of hotlines shows that men rarely seek help, in particular due to social stereotypes, distrust of the law enforcement system and the lack of an appropriate support infrastructure. The most common forms of violence against men are psychological, physical and economic, while sexual violence is almost never recorded in legal practice. The article emphasizes that the stereotypical model of a “strong man” exerts significant psychological pressure on victims, forcing them to remain silent about violence. Such circumstances contribute to the latency of this category of offenses and significantly distort the overall picture of domestic violence in the state. The authors point to the need for a systemic response by the state, which should include not only legal, but also educational and social instruments. Social stereotypes, statistics of appeals, as well as legislative and institutional mechanisms for responding to such cases are analyzed. The issue of the legal invisibility of this category of victims is raised and directions for improving state policy on the protection of male victims of violence are proposed. The positive foreign experience of the United Kingdom, Canada and Hungary, where gender-neutral policies to combat domestic violence have already been implemented, is considered. As a result, a conclusion is made about the urgent need to rethink public perceptions and legal approaches in order to form a truly inclusive system of protection of victims, regardless of their gender, age or social status. The conclusions are made that in order to effectively combat violence against men, it is necessary to improve legislation, expand the system of specialized support services, conduct large-scale information campaigns to overcome gender stereotypes, and also introduce accounting of cases of domestic violence taking into account the gender of the victims. Such an approach will contribute to the formation of a truly inclusive mechanism for the protection of human rights in Ukraine.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".