Infanticide in Female Forensic Patients:The View from the Evolutionary Standpoint
Bibliographic record
Abstract
Evolutionary theory predicts that very young mothers would be more likely to kill an infant than older women, given that the younger mother has a much greater ability to "replace" the dead child through subsequent pregnancies and thus to produce offspring for the next generation. Evolutionary theory also predicts that a woman would be more likely to kill a child if the child was obviously defective, the pregnancy was the result of incest or rape, or if the mother's means of supporting the child were severely compromised. The authors hypothesized that mentally ill mothers would behave in a way that differed significantly from evolutionary expectations, i.e., that they would be more likely to kill children who were older than those killed by mothers in the general population and that the mothers themselves would be likely to be older than mothers in the general population when the murders occurred. To test this hypothesis, the authors compared infanticides (both filicides and neonaticides) committed by mentally ill mothers with those committed by mothers in the general population. They examined two samples: 1) all cases of maternal infanticide from the Mid-Hudson Forensic Psychiatric Hospital from 1978 (when the hospital began admitting female patients) through the year 2000 and 2) a general population sample from a 10-year Canadian study reported by Daly and Wilson in 1998. The authors focused on the following variables: ages of the mothers, ages of the child-victims, whether the pregnancy was the result of rape or incest, whether the child had significant behavioral or physical problems, and whether there were problems supporting the child (e.g., having no partner, poverty, mother's lack of education). The results of the analyses supported the authors' hypothesis about ages of mothers and children. The mentally ill mothers in the Mid-Hudson sample were generally older when they killed their children and the children who were killed were generally older than in the Daly and Wilson general population sample (where the majority of the cases involved neonaticide and the mothers were generally younger than 25 years of age). The three factors, poverty, low education level (or low intellectual capacity), and lack of a spouse were common in both samples. Findings concerning cultural factors, motives, and methods used will be presented in separate publications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".