Foreign experience in applying a multidisciplinary approach to preventing and combating domestic violence
Bibliographic record
Abstract
The article presents the results of scientific research on a comprehensive analysis of the problem of combating and preventing domestic violence in the family. Using the example of foreign experience, in particular, the experience of the USA and Canada, the author substantiates the expediency of using a multidisciplinary approach in the formation of state policy to reduce the number of cases of domestic violence. To confirm the importance of the chosen research topic, the authors of the article provide relevant statistical information provided by law enforcement agencies of Ukraine in 2024. Based on a detailed study of the available information sources, it has been established that the concept of a multidisciplinary approach should be understood as a modern methodology and a unique strategy for conducting scientific research, which, in order to achieve a common goal and solve specific tasks, necessarily involves the use of both theoretical and practical knowledge from various fields of domestic and foreign science. In other words, the approach we have mentioned is based on the integration of various author’s models aimed at effectively addressing a particular relevant and important issue. Authors emphasize that a multidisciplinary approach to the formation of the state policy on preventing and combating domestic violence will be based on the interaction of social, legal, medical, psychological and educational spheres of society. The authors of the article also trace the connection between the study of domestic violence and such sciences as sociology, criminology and pedagogy. Legal communication also plays an equally important role in the application of a multidisciplinary approach. It is also established that the common features of the organizational and legal mechanism of the United States and Canada in combating the increase in the number of cases of domestic violence are the use of regulations adopted within certain administrative-territorial units (states and provinces, respectively), consideration of cases within special courts, and the issuance of protection orders by state authorities. These states also have a wide system of shelters that specialize exclusively in providing the necessary assistance to victims of domestic violence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".