An Empirical Study on Cases of Domestic Violence: Samples of 212 Cases From 22 Provinces, 4 Municipalities, and 4 Autonomous Regions in China
Bibliographic record
Abstract
Domestic violence is one of the most widespread forms of gender-based violence on China Judgments Online. The author conducts statistical analysis on 212 cases of “domestic violence” and explores the gender ratio of perpetrators of domestic violence and the involvement of relevant government departments and organizations. By adopting a comparative analysis method, statistical and comparative analysis of data on the types of violence, the proportion of public prosecution cases, the distribution of sentencing, and the sentencing circumstances of the defendant’s lighter penalties are used to resolve domestic violence. At the same time, the results of this survey will also provide the reader with an intuitive impression of the situation of domestic violence in China and provide some main suggestions for anti-domestic violence practice. This study shows that telephone helplines, police intervention, and the court system is the most deterrent and effective way to stop domestic violence. Only in this way can domestic violence be dealt with promptly and effectively.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".