АНАЛІЗ ЗАРУБІЖНИХ ПРАКТИК ПРОТИДІЇ ДОМАШНЬОМУ НАСИЛЬСТВУ В ПЕРІОД ПАНДЕМІЇ COVID-19
Bibliographic record
Abstract
The relevance of the research topic is justified by the growing number of cases of domestic violence in the period of lockdown restrictions during the COVID-19 pandemic introduced by governments around the world. These include: lockdown, restrictions on social contacts and mobility. They aim at slowing down the spread of COVID-19. Financial insecurity, increased stress due to changes in typical daily behavior and social isolation, possibility of perpetrators to control their victims in everyday life during long period of time have resulted in increased level of aggression and growing number of cases of domestic violence. The international community recognizes domestic violence as one of the most common violations of human rights and freedoms of women, men, the elderly persons, children. Almost everywhere in the world, governmental agencies and various civil society organizations consolidate their effort in order to address this problem, emphasizing its concealment and complexity, as well as gaps in legislation framework regulating prevention and combating such violations. This article analyzes the best international practices of addressing domestic violence during the pandemic, as study of these practices can be useful to Ukrainian society for developing its own programs to combat domestic violence in the context of the COVID-19 pandemic. Due to specific objectives of the research, a theoretical analysis of the scientific literature and foreign Internet sources was conducted to find out specific measures taken in different countries to address domestic violence during the COVID-19 pandemic. We analyzed practices of combating domestic violence in Canada, Sweden, the Czech Republic, Moldova, and Belarus and identified key actions taken by governments and leading civil society organizations in these countries. The selected practices encourage critical assessment, deeper study and consideration of implementing the best of them in Ukraine.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".