Commentary: Homicide-suicide in older adults--cultural and contextual perspectives.
Bibliographic record
Abstract
The authors comment on "Domestic Homicide and Homicide-Suicide: The Older Offender" by Bourget et al., who learned that after a domestic homicide in Canada, the older offender frequently commits suicide. The authors comment on the ubiquity of single homicide-suicide across cultures, the incidence of single homicide-suicide in various cultures, the common patterns and differences in single homicide-suicides across cultures, ethnic and gender differences in single homicide-suicide within different cultures, characteristics of the phenomenon of mass murder followed by suicide and ethnic differences within this type of homicide-suicide, and differences in suicidal patterns in different cultures. Suicide and suicide preceded by homicide (single or multiple) are so rare, it is currently impossible to draw any substantive conclusions about the incidence of these phenomena in various contexts; however, ideas for consideration in addressing homicide-suicide are provided.
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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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.057 | 0.037 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".