Overkill in forensic medicine: A systematic review
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Overkill, a term often used to describe the infliction of massive injuries by far exceeding the amount necessary to kill the victim, is a key, albeit underdefined, concept in forensic science. This systematic review aims to consolidate existing literature on overkill and to provide a comprehensive understanding of how the phenomenon contributes to criminal investigations. METHODS: A stringent and comprehensive literature review of peer-reviewed articles using online databases (Google Scholar and PubMed) was conducted following PRISMA 2020 guidelines. Risk of bias was assessed using the Newcastle-Ottawa scale. RESULTS: The review yielded 214 potential results. These were further assessed for relevance and eligibility and finally an in-depth investigation of 24 articles was conducted. Findings suggest a potential association between overkill and deep-seated psychological and emotional factors, although derived from interpretive case analyses rather than validated empirical models. A likely relevance to offender profiling and legal sentencing is also explored. CONCLUSIONS: We proposed that overkill emerges at the intersection of forensic science, psychology and sociology and offers a unique lens for understanding the dynamics and the motivation behind violent homicides. To enhance the reliability and applicability of future findings, research should focus on diverse contextual backgrounds and prioritize the development of standardized classification criteria.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".