Revenge filicide: An international perspective through 62 cases
Bibliographic record
Abstract
Filicide is the purposeful killing of one or more children by a parent, step-parent, or other parental figure. Revenge filicide is a poorly understood, rare form of filicide in which a parent murders their child to cause emotional harm to the child's other parent. This descriptive study presents an international case series consisting of 62 revenge filicide cases from nine countries. Perpetrators were about equally likely to be male or female. Over half of the sample had an active mental disorder of any type, most often a personality disorder. A history of domestic violence was common. The mean victim age was 6 years. They were typically killed by asphyxiation, firearms, or knives, and preschool age children were targeted most frequently. In half of the cases, more than one child was murdered. Post-crime suicidal behavior was commonplace, and one-third of the sample died by suicide. Four revenge filicide subtypes were identified: rejection, custody/visitation dispute, infidelity/jealousy, and argument/conflict. These categories may prove useful in future research and for helping to identify children at high risk of becoming filicide victims. All surviving offenders were criminally convicted, and in only one case was a mental health defense successful in lessening culpability. Notable similarities and differences between the US and international cases are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".