Discussion on the Determination of Justifiable Defense in Domestic Violence
Bibliographic record
Abstract
In today’s society, domestic violence has stood out as a major social concern, given its profound influence on public harmony and stability. Severe physical and psychological damage is a common plight for victims, and under certain circumstances, they may be forced to resort to extreme means to break free from the abusive situation. However, in judicial practice, these defensive measures are often deemed intentional homicide or assault, failing to gain reasonable recognition as legitimate self-defense. Such a scenario leaves victims trapped in an awkward legal dilemma: enduring the abuse passively means continuing their suffering, while resisting actively brings heavy legal risks. China’s Anti-Domestic Violence Law has established a legal system to handle abuse, yet it offers no explicit criteria for the application of self-defense in domestic violence cases. As such, developing legal mechanisms that well balance jurisprudential principles and humanitarian considerations has become an urgent mission.
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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 | 0.018 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.016 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".