The Difference in the Perceived Effectiveness of Preventative Measures Against Domestic Violence in the Russian Federation and the United States of America
Bibliographic record
Abstract
Domestic violence (intimate partner violence, IPV) is a pervasive issue in both the Russian Federation and in the United States. Despite the fact that they both deal with significant levels of both reported and unreported IPV, the two countries have a very different legal approach to solving the issue: the United States has a set of laws aimed at the prevention and punishment of domestic and intimate partner violence, while the Russian Federation does not. The goal of this paper was to determine whether life in a society that does or does not have a functioning domestic violence law affects the perceived effectiveness of various social and public policy (non-legal) preventative measures. For these purposes, six academic articles on the issue from both Russian and American/Canadian research journals were reviewed, a list of strategies that were deemed potentially effective in both sources was compiled, and a questionnaire was structured around it. Afterwards, 12 women from Russia and the United States were questioned on their opinions regarding the selected preventative measures and whether they would be effective or not. Based on their responses, it can be concluded that the perceived effectiveness of educational, cultural and community-based efforts is significantly higher compared to court and police-based efforts, and they are viewed as more realistic in regards to long-term implementation.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".